- Research article
- Open Access
Comparative transcriptomic analysis of the evolution and development of flower size in Saltugilia (Polemoniaceae)
© The Author(s). 2017
- Received: 8 December 2016
- Accepted: 16 June 2017
- Published: 23 June 2017
Flower size varies dramatically across angiosperms, representing innovations over the course of >130 million years of evolution and contributing substantially to relationships with pollinators. However, the genetic underpinning of flower size is not well understood. Saltugilia (Polemoniaceae) provides an excellent non-model system for extending the genetic study of flower size to interspecific differences that coincide with variation in pollinators.
Using targeted gene capture methods, we infer phylogenetic relationships among all members of Saltugilia to provide a framework for investigating the genetic control of flower size differences via RNA-Seq de novo assembly. Nuclear concatenation and species tree inference methods provide congruent topologies. The inferred evolutionary trajectory of flower size is from small flowers to larger flowers. We identified 4 to 10,368 transcripts that are differentially expressed during flower development, with many unigenes associated with cell wall modification and components of the auxin and gibberellin pathways.
Saltugilia is an excellent model for investigating covarying floral and pollinator evolution. Four candidate genes from model systems (BIG BROTHER, BIG PETAL, GASA, and LONGIFOLIA) show differential expression during development of flowers in Saltugilia, and four other genes (FLOWERING-PROMOTING FACTOR 1, PECTINESTERASE, POLYGALACTURONASE, and SUCROSE SYNTHASE) fit into hypothesized organ size pathways. Together, these gene sets provide a strong foundation for future functional studies to determine their roles in specifying interspecific differences in flower size.
- Exon capture
- Flower Evo-Devo
- Corolla length
- Differential expression
Evolutionary developmental biology, Evo-Devo, aims to understand the origins of morphological variation, which may be caused by variation in genomes or environmental interactions during development [1, 2]. The evolution of similar traits in distinct lineages is often associated with allelic changes in a particular gene , with differential regulation of expression of these genes frequently associated with speciation events or in response to environmental and ecological pressures [4, 5]. Early studies in Evo-Devo relied on comparisons of single candidate genes or a single gene network resulting from a priori knowledge from model systems, which may be distantly related to the species of interest [6, 7]. The advent of transcriptome sequencing has led to an increased push in analysis of non-model species, including fish , invertebrates [9, 10], mammals , and land plants . The number of studies using gene expression to investigate natural variation, responses to stimuli, and the roles that differential gene expression plays in phenotypes is on the rise . Transcriptome-based investigations into aspects of floral evolution in groups without a sequenced genome have addressed the genetics of sex-specific flowers , profiling of outcrossing vs. selfing flowers [15, 16], flower scent , pistillate flowering , and gene regulation in floral pathways [19–21].
These de novo approaches are ideal for studies involving the phlox family (Polemoniaceae). Currently there are no genome-level resources available for any members of Polemoniaceae, and only two studies have been published using transcriptomic data: one on a single species of Phlox  and the other on two species of Saltugilia . Comparative studies focused on closely related species in Polemoniaceae have shown drastic differences in flower size in species of Ipomopsis , Leptosiphon , and Saltugilia . One to six QTL were identified in Ipomopsis for traits such as stamen length, pistil length, corolla tube length, corolla tube width, and anthocyanin abundance . The genetics of organ size in plants, especially flower size, are largely unknown. However, candidate genes associated with flower size differences have been identified in model systems, predominantly Arabidopsis [26–35]. The hypothesized gene networks of many, but not all, of these genes have been reviewed and outlined [35, 36], but work remains, particularly in non-model species, to clarify if – and how – these genes determine flower size.
The goals of this study are two-fold: first, we investigate phylogenetic relationships within Saltugilia using current analytical methods for next-generation sequencing data, as well as a comparison between concatenation and species tree inference methods; second, we investigate patterns of differential gene expression associated with differences in flower size in representatives of the six taxa of Saltugilia (S. australis, S. caruifolia, S. latimeri, S. splendens subsp. grantii, S. splendens subsp. splendens (GH; denoting that this sample was grown in the greenhouse common garden), and S. splendens subsp. splendens (FS; denoting that this is plant material collected in the field)) using RNA-Seq methods. Specifically, we identify differentially expressed genes (1) throughout floral development in each taxon, (2) throughout floral development across all taxa, and (3) between small- and large-flowered species.
Four taxa of Saltugilia and one taxon of Gilia were grown in the greenhouses at the University of Florida from seeds: G. brecciarum subsp. brecciarum, S. australis, S. caruifolia, S. splendens subsp. grantii, and S. splendens subsp. splendens (GH). Additionally G. stellata, G. brecciarum, S. latimeri, and S. splendens subsp. splendens (FS) were collected in California in the spring of 2015 with the help of Tasha LaDoux (University of California Riverside, USA). Finally, three accessions of Gymnosteris, G. nudicaulis and G. parvula (two accessions), were sampled from herbarium material (Additional file 1: Table S1).
A detailed description of the targeted gene capture method for phylogenetic analysis has previously been described . Cleaned sequencing reads were used for 10 accessions spanning Gilia and Saltugilia to infer phylogenetic relationships, with three accessions of Gymnosteris used as outgroups (Additional file 1: Table S1). Briefly, MYbaits (MYcroarray; Ann Arbor, MI, USA) probes for 100 putatively single-copy nuclear genes were designed from reciprocal BLAST of four transcriptomes of closely related taxa (Fouqueria macdouglaii, Phlox drummondii, Phlox sp., and Ternstroemia gymnanthera) available in the 1KP data set (www.onekp.com) using MarkerMiner  to identify single-copy genes. Selected genes were chosen to represent all of the chromosomes in Arabidopsis and to contain large exon regions with few, small intronic regions. Capture products were sequenced on three independent Illumina runs and analyzed using HybPiper . On-target reads of nuclear genes and by-product plastid reads were assembled to individual gene references using default parameters following Burrows-Wheeler Aligner analyses . Contigs for each gene from all taxa were aligned using MAFFT (version 7.245; ) installed on the University of Florida Research Computing cluster using pairwise comparisons with 1000 iterations.
Phylogenetic analysis of assembled loci followed two alternative approaches: concatenation of genes (nuclear and plastid genes separately) and species tree inference using nuclear genes. For the concatenation analysis, aligned gene sequences with at least 450 bp of overlapping sequence were concatenated into a single file using SequenceMatrix . Separate concatenated matrices of 54 nuclear genes and 80 plastid protein-coding genes were then analyzed using PartitionFinder (version 2.0; ) using a greedy algorithm and RAxML (version 8; ) to find the best partitioning scheme for each matrix. The maximum-likelihood tree for each matrix was inferred using RAxML and the previously identified partitions, with an initial 1000 bootstrap replicates before beginning the search for the best tree for each matrix. Because the nuclear and plastid trees differed in topologies (see below), these data were not combined into a single analysis.
Species tree inference was performed in Astral  using the same 54 nuclear genes used for the concatenation analysis. Topologies for individual gene trees were inferred using RAxML with a GRT + G model of evolution. One thousand bootstrap replicates were conducted for each locus, and the inferred maximum-likelihood tree was used for species tree inference. Support for the inferred species tree topology was obtained by running 500 multi-locus bootstrap replicates.
RNA-Seq library preparation and sequencing
Total RNA was extracted from whole flowers using Tri-Reagent following the manufacturer’s directions (Ambion, Austin, TX, USA) from the three developmental stages: half stage (flowers 50% the length of flowers at anthesis), mid stage (flowers 75% the length of flowers at anthesis), and mature stage (directly after anthesis) (Fig. 1g). These developmental stages were the same stages previously used for characterizing cell number and cell size through floral development in Saltugilia . Flowers were collected from two individuals from the same population except for S. latimeri, in which only one flower of the half stage was collected because a second was not available at the time of collection.
Quality of total RNA for each sample was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) at the University of Florida Interdisciplinary Center for Biotechnology Research (ICBR). Library preparation was performed using the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) following the manufacturer’s directions and NEBNext Multiplex Oligos for Illumina Index Primers Set 1 and 2 barcodes (NEB, Ipswich, MA, USA). Transcriptome sequencing was performed at the University of Florida ICBR using a single run on the Illumina NextSeq 500 (2 × 75 bp). The desired sequencing coverage was approximately 30 million reads per sample, but fewer reads were acceptable, given that greater replication has been found to be more beneficial for investigations of differential gene expression than sequencing depth . Raw reads were trimmed and filtered using the pipeline described previously  using Cutadapt  and Sickle , with modifications to the quality parameter (minimum quality score of 26) and length parameter (minimum length of 35 bp) for each read that passed the filtering criteria.
Summary of the quality of the transcriptome for each taxon, including percent of cleaned reads mapped, Trinity genes, total transcripts, percent GC, N50, median contig length, average contig length, and total assembled bases
Saltugilia splendens subsp. grantii
Saltugilia splendens subsp. splendens (GH)
Saltugilia splendens subsp. splendens (FS)
Total Trinity genes
Total Trinity transcripts
Median contig length
Average contig length
Total assembled bases
The generated master reference fasta file was annotated using the Trinotate pipeline by performing a BLASTx analysis against the Swiss-Prot database  to generate Gene Ontology (GO) terms, as well as KEGG pathways using the EggNOG database . The GO terms generated with the BLASTx analysis were imported into CateGOrizer  to categorize and visualize into three broad biological terms using the Plant GO-Slim database: biological processes, molecular functions, and cellular components.
Differential expression analyses were conducted using the Bioconductor DESeq2 package  with a p-value cutoff of 0.05 and a four-fold difference in expression. Only DESeq2 was used given that Rapaport et al.  showed good concordance with this method and other methods such as edgeR  and CuffDiff . Differential expression comparisons within each taxon were performed among the development stages with two biological replicates for each stage.
Comparisons of the same developmental stage (e.g. half, mid, mature) among all taxa were conducted by pooling all taxa from a single stage together as biological replicates. However, based on the low sequencing coverage in samples of S. caruifolia and S. splendens subsp. splendens (GH) where many accessions were well below the targeted 30 million reads, a more conservative approach was undertaken by removing these taxa completely from further analyses. Removal of S. caruifolia and S. splendens subsp. splendens (GH) left eight biological replicates per sample for the mid and mature stages and seven biological replicates for the half stage. This comparison was deemed to have more power to identify differentially expressed genes due to the increased number of biological replicates . Lastly, comparisons between the mature stages of the small-flowered S. australis and S. latimeri against the mature stages of the large-flowered S. splendens subsp. grantii and S. splendens subsp. splendens (FS) were conducted to identify genes that may be associated with observed differences in flower size. Classification as either small-flowered or large-flowered is solely based on relative corolla length, as can be seen in Fig. 1. The half stages were not included in this analysis due to only having one accession of S. latimeri, and the mid stages were not included because this stage did not show many differentially expressed genes in developmental analyses within each taxon.
Targeted capture efficiency and phylogenetic analyses
Of the 100 single-copy nuclear genes for which probes were designed, 94 were sequenced in at least one taxon, with 49 genes captured for all taxa. Average contig length for each gene ranged from 29 bp to 2195 bp. Additionally, 80 protein-coding genes of the plastid genome were sequenced as by-product in all 13 samples, with mean contig lengths ranging from 93 bp to 6816 bp (Additional file 2: Table S2). After MAFFT alignments were completed for each gene, 54 nuclear and 80 plastid protein-coding genes exhibited sufficient overlap (i.e. were captured from at least 7 of the 13 samples) for analysis, spanning the six taxa of interest and the associated outgroups. The final concatenated nuclear data set consisted of 58,318 bp of data, with a data matrix exhibiting only 20.67% missing data. The plastid data set consisted of 64,573 bp of data with only 1.53% missing data.
The unfiltered, assembled master de novo reference compiled from data from all six taxa consisted of 969,222 Trinity genes (or clusters of isoforms that share significant overlap) and 1,222,113 total transcripts (Trinity genes + isoforms), with an N50 of 383 bp and an average contig size of 387 bp. For each taxon, 61.05-80.88% of all reads mapped back to the master reference assembly, resulting in assemblies containing 71,296 to 110,965 transcripts filtered at the 1 TPM level representing the full transcriptome of that taxon (Table 1) and an N50 value ranging between 446 and 668 bp. Individual developmental stages varied in percent of reads mapping to the reference, ranging from 50.19 to 85.74%, with all six samples of S. splendens subsp. splendens (GH) mapping at 65% or lower (Additional file 3: Table S3), substantially lower than the majority of samples. The lowest percent mapped was the mature stage of S. caruifolia plant 4, which also contained the fewest reads that passed the cleaning criteria. The total number of transcripts for each developmental stage also varied, ranging from 41,774 to 136,467 transcripts (Additional file 3: Table S3). The annotation analyses resulted in 23.1% of all transcripts reporting a Swiss-Prot top BLASTx hit, 18.0% having a functional annotation from the EggNOG database, and 23.4% annotated with GO terms of known function (Dryad doi:10.5061/dryad.mc270). Of the annotated GO terms, 16.8% were molecular function genes with 1178 unique terms, 12.30% were cellular component genes with 859 unique terms, and 2.5% were biological process genes with 179 unique terms.
The more conservative approach involving the removal of both S. caruifolia and S. splendens subsp. splendens (GH) produced a total of 6682 clusters, with 6600 defined as putatively orthologous, and an additional 2712 single-copy gene clusters (Fig. 3b). Individual taxon clusters were similar to the previous analysis, with S. australis containing 4140, S. latimeri 6293, S. splendens subsp. grantii 4552, and S. splendens subsp. splendens (FS) 6196 clusters. Almost half of all clusters were shared among the four taxa (3187), with the two field samples again sharing more clusters than any other pair of taxa (1456). The clusters shared among all four taxa did not show any enriched GO categories, while the shared clusters in the two field samples show four enriched terms: lateral root morphogenesis, NAD+ ADP-ribosyltransferase activity, regulation of gene expression, and response to other organisms.
The large-flowered taxa, S. splendens subsp. grantii and S. splendens subsp. splendens (FS), share 62 protein clusters, with 20 showing enriched GO terms involved with activity associated with nutrients or elements (e.g. sulfur dioxygenase activity, nitric oxide biosynthetic process, cellular response to azide, etc.). One enriched GO term, tubulin complex, shows a possible direct link to larger flowers with known functions in microtubule formation .
Each of the six taxa showed transcripts differentially expressed throughout floral development, ranging from four to 10,368 transcripts across the three developmental stages: S. australis (652), S. caruifolia (181), S. latimeri (1032), S. splendens subsp. grantii (4), S. splendens subsp. splendens (GH) (973), and S. splendens subsp. splendens (FS) (10,368) (Additional file 4: Table S4). Of the five taxa that included all three stages, S. australis and S. splendens subsp. splendens (FS) show the mature stage being more similar to the mid stage than either is to the half stage by clustering transcript expression levels (Additional file 5: Fig. S1). In two other taxa, S. splendens subsp. grantii and S. splendens subsp. splendens (GH), the mid and half stages are more similar to each other than either is to the mature stages. In S. caruifolia, the two samples from the mid and mature stages are not identified as being most similar, likely due to insufficient reads for these accessions.
Very few, if any, of the transcripts that were differentially expressed through development within a species were in the broad category of biological process, with the majority of differentially expressed transcripts consisting of cellular component genes (Additional file 4: Table S4). Based on the cellular profile throughout development of flowers in Saltugilia , extra attention was focused on genes associated with maintenance and modification of the cell wall, as well as auxin and gibberellin hormone pathways. In S. australis, three unigenes associated with the cell wall are upregulated in the mature flowers compared with the half-stage flowers, and three unigenes are upregulated in mature flowers compared with mid-stage flowers, with two of the unigenes always showing upregulation in mature flowers compared to any other stage. Flowers of S. caruifolia, S. splendens subsp. grantii, and S. splendens subsp. splendens (GH) show one unigene associated with the cell wall upregulated in mature flowers compared with half-stage flowers. In S. latimeri, five unigenes associated with the cell wall and one unigene associated with auxins are upregulated in mature flowers compared to half-stage flowers.
The taxon with the most upregulated unigenes, S. splendens subsp. splendens (FS), shows 21 unigenes associated with the cell wall, four auxin-activated signaling genes, 22 unigenes encoding auxin-induced proteins, and four unigenes associated with gibberellins upregulated in mature flowers compared to half-stage flowers. Comparisons between mature and mid flowers show six unigenes associated with the cell wall and one encoding an auxin-induced protein upregulated in mature flowers. All differentially expressed transcripts, along with the top BLASTx hit, EGGnog annotation, and the top GO category are found in Additional file 6: Table S5.
Of all the differentially expressed unigenes identified through development within taxa, 28 unigenes, including two biological process genes, eight cellular component genes, four molecular function genes, and 14 unannotated transcripts, are differentially expressed in the mature stages compared to the mid stage in at least two taxa. One gene, a cell wall-associated gene with the top BLASTx hit of POLYGALACTURONASE, is upregulated in all taxa except S. splendens subsp. splendens (GH). In comparisons of half-stage and mature flowers in all taxa, 47 unigenes, including 10 cellular component, 11 molecular function, and 26 unannotated unigenes, are upregulated in at least two taxa. One unigene, with the top BLASTx hit of Tyrosine/DOPA decarboxylase, is upregulated in all four taxa that show differential expression between the half-stage and mature flowers. Three unigenes, including the cell wall gene POLYGALACTURONASE, a molecular function gene with a top BLASTx hit of 1-aminocyclopropane-1-carboxylate oxidase, and another unannotated gene, are upregulated in three of the four taxa.
Fifteen unigenes that were shared in comparisons both within and among taxa for different developmental stages, including the Trinity Gene ID, top BLASTx hit, functional EggNOG annotation, and Gene Ontology (GO) term
Trinity Gene ID
Mini zinc finger protein 2
ZF-HD homeobox protein At4g24660-like
Cellular component: cytoplasm
Alkane hydroxylase MAH1
Cellular component: endoplasmic reticulum
Chaperone protein dnaJ 8
ATP binding to DnaK
Cellular component: chloroplast
Cellular component: endoplasmic reticulum membrane
Stigma-specific STIG1-like protein 4
Stigma-specific protein, Stig1
Cellular component: cell wall
Cellular component: cell wall
Flowering-promoting factor 1
Positive regulation of flower development
Molecular function: 5-hydroxyfuranocoumarin 5-O-methyltransferase activity
Tyrosine/DOPA decarboxylase 5
Molecular function: aromatic-L-amino acid decarboxylase activity
Molecular function: sucrose synthase
Molecular function: flavin adenine dinucleotide binding
The comparisons of the mature-stage flowers in the small- and large-flowered taxa resulted in 217 transcripts upregulated in the large-flowered taxa, with 100 transcripts having functional annotation. These include seven unigenes associated with cell wall modification and maintenance, including a polygalacturonase inhibitor and pectinesterase inhibitor. All of the cell wall unigenes appear to be upregulated in one of the two large-flowered species, mostly S. splendens subsp. grantii. However, two unigenes, a putative beta-D-xylosidase and EXORDIUM-like 2, are upregulated in both large-flowered taxa and may be associated with the observed larger flowers, with these transcripts functioning in secondary cell wall formation, as well as cell expansion. Additionally, all of the previously mentioned candidate genes from either Arabidopsis annotations or from previously hypothesized organ size pathways appear to show differences in mean expression between the large- and small-flowered taxa, but the differences were not significant, with an adjusted p-value of 0.997. The small-flowered taxa show 251 transcripts upregulated, with 121 of these transcripts annotated. The annotated transcripts include 12 unigenes associated with cell wall modification and maintenance and one repressor of early auxin response genes, but no unigenes associated with gibberellin activity.
Saltugilia has long been taxonomically difficult, with previous phylogenetic studies using a combination of sampling approaches, including a small number of loci and one individual per taxon , a small number of loci and many individuals per taxon , or many loci but only one individual per taxon . The recently published HybPiper  used in this study is well suited for assembling contigs from hybrid-enriched data. Previous analyses used up to 90 loci , but these matrices included 37.2% missing data and many ambiguities (e.g. R, G, Y). Here only 54 loci were utilized, reducing the amount of missing data from 37.2 to 20.67%, even though missing data typically have been shown not to alter relationships substantially [67–69].
Previous studies of Saltugilia have reconstructed different relationships, with incongruences observed between nuclear and plastid data [23, 39, 66]. In our analyses based on 80 plastid protein-coding genes, S. australis is sister to S. latimeri. This relationship was also recovered by Weese and Johnson  in trees based on combined nuclear and plastid genes and may result from natural hybridization events between S. australis and S. latimeri, which can produce vigorous hybrids [38, 40]. Both nuclear and plastid data show a well-supported relationship between S. splendens subsp. grantii and S. splendens subsp. splendens (FS), with the addition of S. caruifolia in the clade. The placement of S. splendens subsp. splendens (GH) outside of Saltugilia is supported by multiple lines of evidence, including cell morphology comparisons , gross flower morphology, and transcriptome comparisons. From the inferred phylogeny of nuclear genes, based on both concatenated and species tree analyses, there appears to be a single transition from smaller flowers (S. australis and S. latimeri) to the larger flowers of S. splendens and S. caruifolia.
Transcriptome comparisons and annotation
The filtered transcriptomes for each taxon contained between 51,020 and 92,672 Trinity genes. This is on par with recent transcriptome studies of flowers showing a range of 56,791 to 83,580 unigenes [14–16, 20, 21, 70]. The estimated number of genes for any transcriptome varies, with an average of 40,901 genes in a variety of tissues of Glycine max (soybean) and an average of 21,492 genes in Zea mays (corn) . The estimated number of genes also varies by tissue type, with blueberry (Vaccinium section Cyanococcus) expressing 27,483 unigenes in leaves, 66,610 unigenes in berries, and 72,754 unigenes in developing flower buds .
Fewer than 25% of all transcripts in the Saltugilia reference were annotated, while other studies have shown a third or more of the transcriptome profile being unannotated [72–74]. The poor annotation for Saltugilia is likely due to the lack of genomic resources for Polemoniaceae, with the closest sequenced genomes in members of Ericales, including Actinidia chinensis (kiwi), Primula vulgaris (primrose), Rhododendron williamsianum, and Vaccinium corymbosum (blueberry) (CoGepedia; https://genomevolution.org), which have limited annotations. Of the total annotated transcripts, 16.8% are molecular function genes, 12.3% are cellular component genes, and 2.5% are biological process genes. Even though the molecular function genes form the largest category, the majority of transcripts that appear to be differentially expressed by at least a four-fold change are the cellular component genes (Additional file 4: Table S4).
Most comparisons among the three developmental stages in all six taxa resulted in differentially expressed transcripts with at least a four-fold difference. The greatest number of differentially expressed transcripts occurred between stages in the two taxa collected in the field, S. latimeri and S. splendens subsp. splendens (FS), which is consistent with studies in natural settings finding novel expression in otherwise silent genes relative to studies in controlled environments . In general, more transcripts were differentially expressed between the mature and the half-stage flowers than between either of the other two developmental stages. The large number of differentially expressed genes is associated with increased cell size between the half-stage and mature flowers . During development of any given taxon, the number of differentially expressed transcripts between the half-stage and mature flowers ranged from four to 418, excluding the 7182 transcripts from S. splendens subsp. splendens (FS). When all taxa were used as biological replicates for each of three stages, 784 transcripts were differentially expressed between the half and mature stages, with 514 transcripts upregulated in half-stage flowers and 270 transcripts upregulated in mature flowers. The observed increase in differentially expressed transcripts supports the claim that discovery of differentially expressed genes is easier with increased biological replicates, even at the cost of lower depth of sequencing .
The four unigenes highlighted in Fig. 4 appear to have direct implications for differences in flower size in Saltugilia based on their functional annotations and hypothesized organ size pathways . In this pathway, organ growth occurs via cell elongation and/or cell proliferation, with cell proliferation directly influenced by growth-promoting factors. In Saltugilia, we found a homolog of FLOWERING-PROMOTING FACTOR 1, which has been shown to be involved in gibberellin signaling and may lead to enhanced responsiveness to gibberellins , with additional implications for changes in epidermal cell shape and formation of trichomes of leaves in overexpression studies . The cell wall gene PECTINESTERASE has previously been shown to play a role in cell wall extension [77, 78]. The second cell wall gene, POLYGALACTURONASE, also known as pectin depolymerase, has been shown to weaken the pectin network, which gives strength to plant cell walls [79, 80]. Observed expression of POLYGALACTURONASE in mature flowers is associated with the formation of jigsaw cells , which require cell wall remodeling involving both pectin and cellulose . The increased expression of SUCROSE SYNTHASE is important given that sucrose has been found to be abundant in flower petal tissue and is the first regulatory enzyme leading to starch biosynthesis in sucrose sink tissues (i.e. petals) [82, 83].
Many candidate genes associated with flower size have been identified in Arabidopsis. Putative networks and relationships among many of these have been reviewed previously [35, 36], yet most of these genes have not been investigated beyond model systems. Four of these genes, BIG BROTHER, GASA, LONGIFOLIA, and BIG PETAL, show differential expression between developmental stages in Saltugilia. BIG BROTHER has previously been shown to be a repressor of organ size by restricting the duration of cell proliferation [32, 36]. Taking into account that early growth of flowers primarily consists of cell division and later growth is due to cell expansion [84, 85], observing upregulation of BIG BROTHER in mature flowers is expected, given that the cells are primarily elongating at this stage. Previous analyses of Saltugilia suggest that by the half stage (50% anthesis) in flower development, increases in flower size are predominantly due to increases in cell size and not changes in cell number .
The identified member of the gibberellin-regulated gene family GASA showed higher expression in mature flowers than in any other stage. This pattern of expression is different than observed in Arabidopsis, where GASA showed higher expression in developing flower buds . Later stages of flower development in Saltugilia are marked with increased cell elongation, especially in the elongated and jigsaw cells of the corolla tube . LONGIFOLIA shows higher expression in half-stage flowers than in any other stage. Overexpression analyses of this gene in Arabidopsis resulted in elongated floral organs due to increased polar cell elongation rather than increased cell number . The final candidate gene, BIG PETAL, exhibits higher expression in half-stage flowers. This pattern is opposite from expected, given that this gene restricts the expansion of petal cells in Arabidopsis . The observation of the two identified repressor genes, BIG BROTHER and BIG PETAL, showing temporal differences in expression patterns is consistent when the cellular development of the flower is taken into consideration. With BIG BROTHER restricting cell division, higher expression in later stages is consistent with a pattern of cell elongation later in development. Likewise, BIG PETAL restricts cell elongation early in flower development when cell division is the primary driver of flower size differences.
All genes identified above fit into previously described organ size pathways . Previous analyses of cell size show that early in development, cells increase in area, but change more substantially in overall shape, becoming less circular and more elongated . Therefore, we hypothesize that BIG PETAL is inhibiting elongation of cells in the corolla tube, especially by changing these cells from elongated cells to jigsaw cells later in development, while LONGIFOLIA is acting on these same cells to make them less circular with a larger area. Later in development, BIG PETAL and LONGIFOLIA are downregulated, allowing cells to expand with the increased expression of SUCROSE SYNTHASE, FLOWERING-PROMOTING FACTOR 1, GASA, PECTINESTERASE, and POLYGALACTURONASE. SUCROSE SYNTHASE provides the sucrose necessary for cellular growth  and enhances the effects of GASA , as well as the targeted materials for FLOWERING-PROMOTING FACTOR 1. Higher expression of both PECTINESTERASE and POLYGALACTURONASE later in flower development may allow for the modification of the cell walls in the corolla tube to transition from elongated to jigsaw cells by breaking down sections of the cell wall for modification and laying down more pectin to strengthen the walls. During cell elongation, BIG BROTHER is also upregulated to cease cell division.
Incorporating phylogenetic relationships with differences in flower size suggests that larger flowers are the derived state in Saltugilia (Fig. 2). Only two of the eight candidate genes discussed above, FLOWERING-PROMOTING FACTOR 1 and BIG PETAL, exhibit expression patterns expected based on the evolution of large flowers from small flowers. Along the phylogeny, there appear to be two transitions of increased expression of FLOWERING-PROMOTING FACTOR 1, with an increase between mature flowers of S. australis and S. latimeri, followed by a second transition between mature flowers of S. latimeri and mature flowers in the clade giving rising to S. splendens subsp. grantii and S. splendens subsp. splendens (FS) (Fig. 4). BIG PETAL follows a similar evolutionary trajectory, with a single increase in relative expression from the small-flowered taxa to the large-flowered taxa. This same trajectory is also observed in Beta-D-xylosidase and EXORDIUM-like 2, which show differential expression between the small- and large-flowered taxa.
Four of the identified genes were previously characterized in Arabidopsis, with implications for floral organ size, while four others play a role in previously hypothesized pathways of mature organ size. Future functional analyses of these genes in non-model systems will help characterize the genetic control of differences in flower size. New phylogenetic relationships of Saltugilia are also presented based on current next-generation sequencing methods and recently developed approaches to phylogeny reconstruction. Few of the genes that are upregulated in mature relative to immature flowers also have higher mean expression levels in large-flowered versus small-flowered taxa, suggesting that the genetic control of changes in flower size during development may also govern interspecific differences in flower size, although these differences in mean expression levels are not significant. Further studies mapping shifts in gene expression to specific regions of the corolla should improve our ability to identify the interspecific differences in gene expression associated with flower size.
The authors thank Tasha LaDoux, Evan Meyers, Duncan Bell, Amy Litt, and Elizabeth McCarthy for help in collecting plant material in the field and Leigh Johnson for supplying seeds and valuable insights, as well as Kayla Ventura and Rebecca O’Toole for help with material preparation and lab work. The authors thank Adam Payton, Sarah Carey, Clayton Visger, Srikar Chamala, Grant Godden, and Heather Rose Kates for helpful discussion of transcriptome and phylogenetic analyses methods and Marissa Gredler for helpful discussion during project planning. The authors thank Martin Cohn, David Clark, and David Oppenheimer for helpful comments on the manuscript, as well as two reviewers for their helpful comments/suggestions.
This work was funded by the following grants: NSF DDIG DEB1406650 (PS and JL), BSA Graduate Student Research Grant (JL), Sigma Xi Grant in Aid of Research (JL), Southern California Botanist Research Grant (JL), and a microMorph training grant (JL).
Availability of data and materials
Raw sequencing reads generated and analyzed during this study for both phylogenetic and transcriptome analyses are deposited in the Sequence Read Archive (www.ncbi.nlm.nih.gov/sra) (Additional file 1: Table S1). Assemblies, fasta files, and annotation reports are deposited in Dryad (doi:10.5061/dryad.mc270).
JL carried out the lab work and analyses for the phylogenetic and transcriptome analyses. JL, DS, and PS designed the study and drafted the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Hall BK. Evo-Devo: evolutionary developmental mechanisms. Int J Dev Biol. 2003;47:491–5.PubMedGoogle Scholar
- Roux J, Rosikiewicz M, Robinson-Rechavi M. What to compare and how: comparative transcriptomics for Evo-Devo. J Exp Zool B Mol Dev Evol. 2015;324:372–82.View ArticlePubMedPubMed CentralGoogle Scholar
- Martin A, Orgogozo V. The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation. Evolution. 2013;67:1235–50.PubMedGoogle Scholar
- Brem RB, Yvert G, Clinton R, Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–5.View ArticlePubMedGoogle Scholar
- Schraiber JG, Mostovoy Y, Hsu TY, Brem RB. Inferring evolutionary histories of pathway regulation from transcriptional profiling data. PLoS Comput Biol. 2013;9:e1003255.View ArticlePubMedPubMed CentralGoogle Scholar
- Edmunds RC, Su B, Balhoff JP, Eames BF, Dahdul WM, Lapp H, et al. Phenoscape: identifying candidate genes for evolutionary phenotypes. Mol Biol Evol. 2016;33:13–24.View ArticlePubMedGoogle Scholar
- Mallarino R, Abzhanov A. Paths less traveled: evo-devo approaches to investigating animal morphological evolution. Annu Rev Cell Dev Biol. 2012;28:743–63.View ArticlePubMedGoogle Scholar
- Schunter C, Vollmer SV, Macpherson E, Pascual M. Transcriptome analyses and differential gene expression in a non-model fish species with alternative mating tactics. BMC Genomics. 2014;15:167.View ArticlePubMedPubMed CentralGoogle Scholar
- Feldmeyer B, Wheat CW, Krezdorn N, Rotter B, Pfenninger M. Short read Illumina data for the de novo assembly of a non-model snail species transcriptome (Radix Balthica, Basommatophora, Pulmonata), and a comparison of assembler performance. BMC Genomics. 2011;12:317.View ArticlePubMedPubMed CentralGoogle Scholar
- Oppenheim SJ, Baker RH, Simon S, DeSalle R. We can’t all be supermodels: the value of comparative transcriptomics to the study of non-model insects. Insect Mol Biol. 2015;24:139–54.View ArticlePubMedGoogle Scholar
- Degletagne C, Keime C, Rey B, de Dinechin M, Forcheron F, Chuchana P, et al. New method for the analysis of heterologous hybridization on microarrays. BMC Genomics. 2010;11:344.View ArticlePubMedPubMed CentralGoogle Scholar
- Strickler SR, Bombarely A, Mueller LA. Designing a transcriptome next-generation sequencing project for a nonmodel plant species1. Am J Bot. 2012;99:257–66.View ArticlePubMedGoogle Scholar
- Alvarez M, Schrey AW, Richards CL. Ten years of transcriptomics in wild populations: what have we learned about their ecology and evolution? Mol Ecol. 2015;24:710–25.View ArticlePubMedGoogle Scholar
- Guo S, Zheng Y, Joung J-G, Liu S, Zhang Z, Crasta OR, et al. Analysis of cucumber flowers with different sex types. BMC Genomics. 2010;11:384.View ArticlePubMedPubMed CentralGoogle Scholar
- Ness RW, Siol M, Barrett SCH. De novo sequence assembly and characterization of the floral transcriptome in cross- and self-fertilizing plants. BMC Genomics. 2011;12:298.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang W, Wei X, Meng H-L, Ma C-H, Jiang N-H, Zhang G-H, et al. Transcriptomic comparison of the self-pollinated and cross-pollinated flowers of Erigeron Breviscapus to analyze candidate self-incompatibility-associated genes. BMC Plant Biol. 2015;15:248.View ArticlePubMedPubMed CentralGoogle Scholar
- Yue Y, Yu R, Fan Y. Transcriptome profiling provides new insights into the formation of floral scent in Hedychium coronarium. BMC Genomics. 2015;16:470.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang Y-J, Liu L-L, Huang J-Q, Wang Z-J, Chen F-F, Zhang Q-X, et al. Use of transcriptome sequencing to understand the pistillate flowering in hickory (Carya Cathayensis Sarg.). BMC Genomics. 2013;14:691.View ArticlePubMedPubMed CentralGoogle Scholar
- Kim J, Park JH, Lim CJ, Lim JY, Ryu J-Y, Lee B-W, et al. Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in Rosa cultivars. BMC Genomics. 2012;13:657.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang W, Steinmann VW, Nikolov L, Kramer EM, Davis CC. Divergent genetic mechanisms underlie reversals to radial floral symmetry from diverse zygomorphic flowered ancestors. Front Plant Sci. 2013;4:302.PubMedPubMed CentralGoogle Scholar
- Galla G, Vogel H, Sharbel TF, Barcaccia G. De novo sequencing of the Hypericum perforatum L. flower transcriptome to identify potential genes that are related to plant reproduction sensu lato. BMC Genomics. 2015;16:254.View ArticlePubMedPubMed CentralGoogle Scholar
- Qu Y, Zhou A, Zhang X, Tang H, Liang M, Han H, et al. De novo Transcriptome sequencing of low temperature-treated Phlox subulata and analysis of the genes involved in cold stress. Int J Mol Sci. 2015;16:9732–48.View ArticlePubMedPubMed CentralGoogle Scholar
- Landis JB, O’Toole RD, Ventura KL, Gitzendanner MA, Oppenheimer DG, Soltis DE, et al. The phenotypic and genetic underpinnings of flower size in Polemoniaceae. Front Plant Sci. 2016;6:1144.View ArticlePubMedPubMed CentralGoogle Scholar
- Nakazato T, Rieseberg LH, Wood TE. The genetic basis of speciation in the Giliopsislineage of Ipomopsis (Polemoniaceae). Heredity. 2013;111:227–37.View ArticlePubMedPubMed CentralGoogle Scholar
- Goodwillie C, Ritland C, Ritland K. The genetic basis of floral traits associated with mating system evolution in Leptosiphon (Polemoniaceae): an analysis of quantitative trait loci. Evolution. 2006;60:491–504.View ArticlePubMedGoogle Scholar
- Herzog M, Dorne AM, Grellet F. GASA, a gibberellin-regulated gene family from Arabidopsis thaliana related to the tomato GAST1 gene. Plant Mol Biol. 1995;27:743–52.View ArticlePubMedGoogle Scholar
- Ben-Nissan G, Weiss D. The petunia homologue of tomato gastl: transcript accumulation coincides with gibberellin-induced corolla cell elongation. Plant Mol Biol. 1996;32:1067–74.View ArticlePubMedGoogle Scholar
- Elliott RC, Betzner AS, Huttner E, Oakes MP, Gerentes D, Perez P, et al. AINTEGUMENTA, an a PETA LAP-like Gene of Arabidopsis with Pleiotropic roles in ovule development and floral organ growth. Plant Cell. 1996;8:155–68.View ArticlePubMedPubMed CentralGoogle Scholar
- Kotilainen M, Helariutta Y, Mehto M, Pollanen E, Albert VA, Elomaa P, et al. GEG participates in the regulation of cell and organ shape during corolla and carpel development in gerbera hybrida. Plant Cell. 1999;11:1093–104.View ArticlePubMedPubMed CentralGoogle Scholar
- Mizukami Y, Fischer RL. Plant organ size control: AINTEGUMENTA regulates growth and cell numbers during organogenesis. Proc Natl Acad Sci U S A. 2000;97:942–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Mizukami Y. A matter of size: developmental control of organ size in plants. Curr Opin Plant Biol. 2001;4:533–9.View ArticlePubMedGoogle Scholar
- Disch S, Anastasiou E, Sharma VK, Laux T, Fletcher JC, Lenhard M. The E3 Ubiquitin Ligase BIG BROTHER controls Arabidopsis organ size in a dosage-dependent manner. Curr Biol. 2006;16:272–9.View ArticlePubMedGoogle Scholar
- Lee YK, Kim G-T, Kim I-J, Park J, Kwak S-S, Choi G, et al. LONGIFOLIA1 and LONGIFOLIA2, two homologous genes, regulate longitudinal cell elongation in Arabidopsis. Development. 2006;133:4305–14.View ArticlePubMedGoogle Scholar
- Szécsi J, Joly C, Bordji K, Varaud E, Cock JM, Dumas C, et al. BIGPETALp, a bHLH transcription factor is involved in the control of Arabidopsis petal size. EMBO J. 2006;25:3912–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Krizek BA. Making bigger plants: key regulators of final organ size. Curr Opin Plant Biol. 2009;12:17–22.View ArticlePubMedGoogle Scholar
- Krizek BA, Anderson JT. Control of flower size. J Exp Bot. 2013;64:1427–37.View ArticlePubMedGoogle Scholar
- Grant V, Grant K. Flower Pollination in the Phlox Family. 1st edition. New York: Columbia University Press; 1965.Google Scholar
- Weese TL, Johnson LA. Saltugilia latimeri: a new species of Polemoniaceae. Madrono. 2001;48:198–204.Google Scholar
- Weese TL, Johnson LA. Utility of NADP-dependent isocitrate dehydrogenase for species-level evolutionary inference in angiosperm phylogeny: a case study in Saltugilia. Mol Phylogenet Evol. 2005;36:24–41.View ArticlePubMedGoogle Scholar
- Grant V, Grant A. Genetic and taxonomic studies in Gilia VII. The woodland Gilias. El Aliso. 1954;3:59–91.Google Scholar
- Chamala S, García N, Godden GT, Krishnakumar V, Jordon-Thaden IE, De Smet R, et al. MarkerMiner 1.0: A new application for phylogenetic marker development using angiosperm transcriptomes. Appl Plant Sci [Internet]. 2015;3. Available from: http://dx.doi.org/10.3732/apps.1400115
- Johnson MG, Gardner EM, Liu Y, Medina R, Goffinet B, Shaw AJ, et al. HybPiper: Extracting coding sequence and introns for phylogenetics from high-throughput sequencing reads using target enrichment. Appl. Plant Sci. [Internet]. 2016;4. Available from: http://dx.doi.org/10.3732/apps.1600016
- Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics. 2009;25:1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Vaidya G, Lohman DJ, Meier R. SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information. Cladistics. 2011;27:171–80.View ArticleGoogle Scholar
- Lanfear R, Calcott B, Ho SYW, Guindon S. Partitionfinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol Biol Evol. 2012;29:1695–701.View ArticlePubMedGoogle Scholar
- Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Mirarab S, Reaz R, Bayzid MS, Zimmermann T, Swenson MS, Warnow T. ASTRAL: genome-scale coalescent-based species tree estimation. Bioinformatics. 2014;30:i541–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu Y, Zhou J, White KP. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics. 2014;30:301–4.View ArticlePubMedGoogle Scholar
- Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetjournal. 2011;17:10–2.Google Scholar
- Joshi NA, Fass JN. Sickle: A sliding-window, adptive, quality-based trimming tool for fastq files [Internet]. 2011. Available from: https://github.com/najoshi/sickle
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc Nature Research. 2013;8:1494–512.View ArticleGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 2012;131:281–5.View ArticlePubMedGoogle Scholar
- Wang Y, Coleman-Derr D, Chen G, Gu YQ. OrthoVenn: a web server for genome wide comparison and annotation of orthologous clusters across multiple species. Nucleic Acids Res. 2015;43:W78–84.View ArticlePubMedPubMed CentralGoogle Scholar
- Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13:2178–89.View ArticlePubMedPubMed CentralGoogle Scholar
- UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015;43:D204–12.View ArticleGoogle Scholar
- Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.View ArticlePubMedGoogle Scholar
- Zhi-Liang H, Bao J, Reecy J, Hu Z. CateGOrizer: A web-based program to batch analyze gene ontology classification categories. Online J Bioinform. 2008;9:108–12.Google Scholar
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.View ArticlePubMedPubMed CentralGoogle Scholar
- Rapaport F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, et al. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol. 2013;14:R95.View ArticlePubMedPubMed CentralGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.View ArticlePubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Kong Z, Hotta T, Lee Y-RJ, Horio T, Liu B. The γ -Tubulin complex protein GCP4 is required for organizing functional microtubule arrays in Arabidopsis thaliana. Plant Cell. 2010;22:191–204.View ArticlePubMedPubMed CentralGoogle Scholar
- Landis JB. Evolutionary development of flower variation in Polemoniaceae: Linking cellular phenotypes, genetics, and pollinator shifts [PhD]. University of Florida; 2016.Google Scholar
- Wiens JJ. Missing data, incomplete taxa, and phylogenetic accuracy. Syst Biol. 2003;52:528–38.View ArticlePubMedGoogle Scholar
- de Queiroz A, Gatesy J. The supermatrix approach to systematics. Trends Ecol Evol. 2007;22:34–41.View ArticlePubMedGoogle Scholar
- Wiens JJ, Tiu J. Highly incomplete taxa can rescue phylogenetic analyses from the negative impacts of limited taxon sampling. PLoS One. 2012;7:e42925.View ArticlePubMedPubMed CentralGoogle Scholar
- Rowland LJ, Alkharouf N, Darwish O, Ogden EL, Polashock JJ, Bassil NV, et al. Generation and analysis of blueberry transcriptome sequences from leaves, developing fruit, and flower buds from cold acclimation through deacclimation. BMC Plant Biol. 2012;12:46.View ArticlePubMedPubMed CentralGoogle Scholar
- García-Ortega LF, Martínez O. How many genes are expressed in a Transcriptome? Estimation and results for RNA-Seq. PLoS One. 2015;10:e0130262.View ArticlePubMedPubMed CentralGoogle Scholar
- Colbourne JK, Pfrender ME, Gilbert D, Thomas WK, Tucker A, Oakley TH, et al. The ecoresponsive genome of Daphnia pulex. Science. 2011;331:555–61.View ArticlePubMedPubMed CentralGoogle Scholar
- Ellison CE, Kowbel D, Glass NL, Taylor JW, Brem RB. Discovering functions of unannotated genes from a transcriptome survey of wild fungal isolates. MBio. 2014;5:e01046–13.View ArticlePubMedPubMed CentralGoogle Scholar
- Jin Y, Cong B, Wang L, Gao Y, Zhang H, Dong H, et al. Differential Gene expression analysis of the Epacromius coerulipes (Orthoptera: Acrididae) Transcriptome. J Insect Sci. 2016;16:42.View ArticlePubMedPubMed CentralGoogle Scholar
- Kania T, Russenberger D, Peng S, Apel K, Melzer S. FPFI promotes flowering in Arabidopsis. Plant Cell. 1997;9:1327–38.View ArticlePubMedPubMed CentralGoogle Scholar
- Melzer S, Kampmann G, Chandler J, Apel K. FPF1 modulates the competence to Øowering in. Plant J. 1999;18:395–405.View ArticlePubMedGoogle Scholar
- Di Matteo A, Giovane A, Raiola A, Camardella L, Bonivento D, De Lorenzo G, et al. Structural basis for the interaction between pectin methylesterase and a specific inhibitor protein. Plant Cell. 2005;17:849–58.View ArticlePubMedPubMed CentralGoogle Scholar
- Fries M, Ihrig J, Brocklehurst K, Shevchik VE, Pickersgill RW. Molecular basis of the activity of the phytopathogen pectin methylesterase. EMBO J. 2007;26:3879–87.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones TM, Anderson AJ, Albersheim P. IV. Studies on the polysaccharide-degrading enzymes secreted by Fusarium oxysporum f. Sp. lycopersici. Physiol Plant Path. 1972;2:153–66.View ArticleGoogle Scholar
- Harholt J, Suttangkakul A, Vibe SH. Biosynthesis of pectin. Plant Physiol. 2010;153:384–95.View ArticlePubMedPubMed CentralGoogle Scholar
- Higaki T, Kutsuna N, Akita K, Takigawa-Imamura H, Yoshimura K, Miura T. A theoretical model of jigsaw-puzzle pattern formation by plant leaf epidermal cells. PLoS Comput Biol. 2016;12:e1004833.View ArticlePubMedPubMed CentralGoogle Scholar
- Aloni B, Karni L, Zaidman Z, Schaffer AA. The relationship between sucrose supply, sucrose-cleaving enzymes and flower abortion in pepper. Ann Bot. 1997;79:601–5.View ArticleGoogle Scholar
- Bolouri Moghaddam MR, Van den Ende W. Sugars, the clock and transition to flowering. Front Plant Sci. 2013;4:22.PubMedGoogle Scholar
- Irish VF. The Arabidopsis petal: a model for plant organogenesis. Trends Plant Sci. 2008;13:430–6.View ArticlePubMedGoogle Scholar
- Hepworth J, Lenhard M. Regulation of plant lateral-organ growth by modulating cell number and size. Curr Opin Plant Biol. 2014;17:36–42.View ArticlePubMedGoogle Scholar
- Yoo M-J, Wendel JF. Comparative evolutionary and developmental dynamics of the cotton (Gossypium hirsutum) fiber transcriptome. PLoS Genet. 2014;10:e1004073.View ArticlePubMedPubMed CentralGoogle Scholar