Bioinformatic prediction of transcription factor binding sites at promoter regions of genes for photoperiod and vernalization responses in model and temperate cereal plants

The genetic basis of flowering time control has been studied extensively in the model plant Arabidopsis (Arabidopsis thaliana) with over 200 putative flowering-related genes being identified [1, 2]. These genes have served as the reference for genome-wide prediction of flowering gene homologs in other plants including a cereal model species Brachypodium (Brachypodium distachyon), and two important cereal crops in short-season cropping regions, wheat (Triticum aestivum) and barley (Hordeum vulgare) [3, 4]. Such an approach assumes evolutionary conservation of flowering genes between Arabidopsis and other plants, but, different flowering pathways show varying degrees of evolutionary conservation between Arabidopsis and cereals [5–9]. The photoperiod pathway, particularly the circadian clock entrainment, is relatively conserved between Arabidopsis and monocot species [10]. For example, a recent study reported that two-thirds of the key circadian clock components are conserved in Arabidopsis and barley [11], including critical photoperiod genes like CONSTANS (CO), EARLY FLOWERING 4 (ELF4), and PSEUDO RESPONSE REGULATORs (PRRs). In contrast, another major flowering-related pathway controlling the vernalization response pathway is reported to be less conserved between Arabidopsis and monocots [5–9].

Like in all other regulated genes, the expression of flowering genes is regulated by regions of non-coding DNA known as cis-regulatory elements (CREs) that contain transcription factor binding sites (TFBSs) to regulate the gene transcription. The two most well-characterized types of CREs are promoters and enhancers [12]. A promoter is about 100–1000 bp long and it is often located at upstream of a transcription start site (TSS) of a regulated gene. A specific DNA sequence in the promoter provides a secure initial binding site for RNA polymerase and for other transcription factors (TFs) that recruit RNA polymerase. Thus the positions and sequences of promoters can be inferred with relative ease from their immediate physical proximity to the regulated genes. On the other hand, while an enhancer is also a short (50–1500 bp) region of DNA that can be bound by TFs (i.e., activators) for transcription regulation of a gene, it is located up to 1 Mbp away from the regulated gene in either upstream or downstream from the TSSin the forward or backward direction. Thus there is currently no single ‘enhancer marker’ for genome-wide identification of enhancers because all conservation- or epigenomics-based predictions show that some enhancer regions are missed (false negatives), and other sequences predicted to be active enhancers cannot be validated by complementary methods (false positives).

Some empirical studies have revealed the effects of sequence variations surrounding TFBSs at promoter regions of the flowering-related genes on the photoperiod sensitivity and vernalization requirement in cereal species. A 2,089 bp deletion in the upstream of the wheat PHOTOPERIOD1 (PPD1) gene (Ppd-A1 and Ppd-D1) can reduce the photoperiod sensitivity, resulting in early heading time [13–15]. Similarly, an insertion of 308 bp and a deletion of 1,085 bp in the upstream region of Ppd-A1 were shown to accelerate heading by 7 – 9 days compared with the photoperiod-sensitive genotype at this gene locus [16]. This photoperiod insensitive phenotype was caused by the removal of one or more regulatory regions, which are involved in TF binding and regulation of PPD1 [14, 16]. Three independent deletions in the promter regions of VERNALIZATION1 (VRN1) in Triticum monococcum caused elevated gene expression and reduced vernalization requirement [17]. Coversely, single-nucleotide polymorphisms (SNPs) in the promoter of VRN1-D and intron deletion resulted in its reduced expression and increased vernalization requirement in wheat [18]. Furthermore, insertion/deletion in the first intron of VRN1 in wheat and barley can also reduce their vernalization requirement [19–21]. Recently, Kippes et al. [22] showed that three adajcent SNPs in a regulatory region of the wheat VRN-D4 first intron disrupt the binding of GLYCINE-RICH RNA-BINDING PROTEIN 2 (TaGRP2), a known repressor of VRN1 expression. The effect of intron deletion is intriguing because introns have been historically considered as junk DNA, though recent work shows that the first intron can also habour regulatory signals [22, 23]. Further sequence analysis showed that variations causing changes in photoperiod sensitivity and vernalization requirement in wheat and barley were not detected in the protein-coding regions of PPD1 or VRN1 [16, 17]. Therefore, TFBS identification, like gene annotation, is also essential to elucidate the molecular basis of flowering transition in plant species.

Experimental techniques are available to identify TFBSs in the promoter regions of target genes, including CHIP-Seq (chromatin immunoprecipitation coupled with massively parallel DNA sequencing) and protein binding microarray (PBM) [24–26]. However, these techniques have limitations such as the GC-content bias and high cost, and require a considerable amount of downstream data processing. Thus, bioinformatic approaches have also been developed for TFBS prediction and they typically depend on position weight matrices (PWMs) corresponding to TFs as the scoring matrices [27–29]. Most PWMs are derived from binding motifs that are determined experimentally for a given TF, and they can be obtained in public databases such as JASPAR, TRANSFAC, and CIS-BP [24, 30, 31]. Additionally, approaches using GC content as DNA free energy profiles to predict TFBSs in plants have also been developed [32, 33].

To date, the TFBS annotation in plant regulatory sequences is still largely limited, with most work being done in model plant Arabidopsis, such as AGRIS [34]. The objective of this study was to predict distributions and related properties of TFBSs in the promoter regions of flowering-related genes in the Arabidopsis genome and the genomes of three cereal species, Brachypodium, wheat and barley. Model cereal plant Brachypodium is included as an effort to bridge the knowledge gap between the well-characterized Arabidopsis genome and the large, complex wheat/barley genomes. Despite the potential significance of enhancers and other CREs in controlling the level of transcription of flowering genes in response to environmental stimuli, this study focused on predicting TFBSs in the promoter regions of the genes only. Our approach consisted of the following steps. First, we identified the genes involved in the pathways of photoperiod (PH) and vernalization (VE), and the pathway integration (PI) genes that control the convergence point of the PH and VE pathways [6, 9, 35] in Arabidopsis, Brachypodium, wheat and barley. Second, we predicted TFBSs in the promoter regions of these genes and assessed the divergence of the TFBSs relative to their coding sequences (CDS) and introns of the orthologous genes. Third, we analyzed the public microarray data sets to assess the relationship between putative TFBSs and gene expression profiles. Finally, we mapped putative TFBSs onto the genes of the PH, VE and PI pathways in the four species, with the three genomes (A, B, and D) of allohexaploid wheat being treated separately.

In this work, we predicted TFBSs in the promoter regions of flowering genes involved in the PH, VE and PI pathways in two model plants, Arabidopsis and Brachypodium, and two important cereal crops, wheat and barley. We chose the two major flowering pathways (PH and VE) and their integrator (PI) for TFBS prediction because these pathways are regulated by the well-known gene regulatory networks [3, 6, 54], but little experimental evidence is available for characterizing how cis- regulatory elements (CREs) in the promoter regions of the genes are interacted with each other to activate or repress the expression of a network of genes along the pathways. For easy identification and comarison within and among the four species, we mapped the predicted TFBSs onto appropriate flowering genes present in the pathways (Fig. 4). In this fahsion, a gene regulatory network is readily formed to visualize the flow of gene regulation within the network (i.e., how one gene regulator is controlled by another in the network of genes within and between the pathways). To the best of our knowledge, this is the first attempt to link the pathways with predicted TFBSs, thereby providing an opportunity for pathway-guided prediction of TFs for specific genes in future studies.

Recent sequencing of several genomes of non-model plants including the large and complex genomes of wheat and barley [52, 55, 56] has allowed for prediction or identification of important genes (e.g., flowering genes) in the crop species based on well-annotated genes in model species such as Arabidopsis or Brachypodium. However, these gene annotation data can only be more effectively utilized if more is learned from patterns and properties of CREs. In our study, we focused on predicting distributions and patterns of TFBSs in the promoter regions of flowering genes. While the promoters are essential for transcription regulation of flowering or other functional genes across the genome, they alone can only produce basal levels of mRNA. Additionally, TFBSs in promoter regions often bind to a set of widely used and highly conserved TFs and thus they are not the major cause of cis-regulatory divergence among different species. In contrast, enhancers often turn on the promoters at specific genomic locations, times, and levels, and that is why they are sometimes known as the “promoters of the promoter.” Enhancers exhibit more interspecific variability and thus they are more often considered to be responsible for cis-regulatory divergence. However, unlike promoters, enhancers are more difficult to be located because they appear to be in upstream (5’), downstream (3’) or in the intron(s) of the gene they regulate; they can also be located far away from the gene.

Recently, a new open chromatin signature-based enhancer prediction system was developed for enhancer identification in Arabidopsis and other plants [57] Genome-wide patterns and distributions of other types of CREs such as silencers and insulators remain poorly understood. Thus, future studies can identify reproducible sequence patterns and genomic locations of TFBSs for enhancers and other less well-characterized CREs for bioinformatic predictions similar to what we did for promoters in this study.

While TFBS prediction is an important first step towards molecular characterization of gene regulation in plant species, it remains more difficult than gene prediction for the following reasons. First, regulatory regions control the transcription of genes but do not directly code for an identifiable product or function. Thus, TFBSs must be predicted from DNA sequences alone. Second, TFBSs are typically short in length, ranging from 5 to 31 nucleotides with an average length of merely 10 bp in eukaryotes [58]. Hence, it can be difficult to predict TFBSs using simple sequence analysis tools such as BLAST. Third, most TFBSs are highly degenerate [59, 60], which are reflected in the different probabilities of the four nucleotides at each position of PWMs. Consequently, a similar promoter sequence can be recognized by different groups of TFs and a TF may bind to more than one motif [61]. To further improve the quality of the predicted TFBSs, we implemented several filtering steps for both input and output files. First, those promoter sequences with large gaps (N’s). were removed from the input file. Second, high-quality PWMs (preferably derived from direct experimental data) were used for TFBS prediction. PWMs from public databases, such as JASPAR, TRANSFAC and CIS-BP [24, 30, 31], might be redundant. For example, even in the same database, identical TFs may sometimes be represented by different matrices that are obtained with different methods [62]. Consequently, redundant or very similar PWMs were removed to reduce the false positive rate. Third, for the FIMO output files, the predicted motifs were examined to only retain the most significant TFBSs or the longest motif covering short ones within the same TF family.

The largest numbers of TFBSs were predicted for MADS-box and CSD TF families, suggesting their important roles in flowering regulation. Several reasons for such TFBS motif abundance in MADS-box and CSD TF families may be speculated. First of all, the large number of hits may be due to the possibility that the PWMs for these TF families are less conservative. To check out this possibility, we calculated the Kullback–Leibler divergence (DKL) indices [63] for all 371 PWMs, corresponding to 345 cognate TFs. The DKL values had a wide range from -0.2179 to -6.5575. A DKL index should be close to zero if the letter (basepair) distribution is close to a uniform distribution (i.e., p- > 0.25); otherwise it would be far from zero. According to this criterion, the MADS-CSD motifs are actually more conservative as their DKL values were < -4.0. So the large number of hits in TFBS motif search is not necessarily caused by the less conservation of PWMs for the MADS-CSD motifs.

There are other possible reasons for the TFBS motif abundance in MADS-box and CSD TF families as well. The roles of MADS-box TFs in flowering control have long been established, and CSD TFs are mainly involved in cold acclimation but some of them are also related to flowering time [64–70]. Furthermore, most MADS-box and CSD motifs overlap in the promoter region of a target gene, suggesting that they might play cooperative functions in the regulation of photoperiod and vernalization responses. Additionally, we found genes with similar number of MADS-box and CSD TFBSs often show similar expression (coexpression) patterns in different tissues and developmental stages. It is somewhat surprising that we predicted the highest density of putative TFBSs in Brachypodium. This might be at least partially due to the larger number (195) of PWMs derived from monocot species than the number (176) of PWMs from Arabidopsis. Another reason might be the higher quality of promoter sequences in Brachypodium than in wheat and barley. The functional implication of higher TFBS density in Brachypodium may be a topic for further research.

It should be noted that only a limited number of PWMs are available in the two monocot crop species in our study, wheat and barley, because few functional genes such as those related to flowering are molecularly characterized in these large, complex genomes. The TFBS prediction based on very few PWMs would be unreliable. For this reason, maize, along a few other species, was added to the list of the monocot species in our initial compiling of PWMs. In particular, maize had 209 PWMs compared to nine PWMs for wheat and six PWMs for barley. Brachypodium is phylogenetically closer to wheat and barley than maize and had a similar number (192) of PWMs to maize, but it is a wild species with a potentially large number of ancient or doimestication genes that have been eliminated or modified from the geneomes of maize, wheat and barley during their domestication and selective breeding [71]. A check based on the DKL index [63] shows a similarity between motifs of maize, wheat and barley. Thus adding maize to the list would have helped to improve the accuracy of the TFBS prediction through borrawing the PWM information from the monocot species such as maize with close phylogeny and similar demostication levels.

The superimposition of TFBS numbers of the major TF families in the flowering pathways allows us to compare the TFBS profiles in promoters of the regulated genes for PH, VE and PI pathways in the four species. For the orthologous genes we determined among the four these species, both the similarity and differences were found. For example, TFBSs of MADS-box TFs were predicted for the orthologous photoperiod gene CO in the four species. On the other hand, the floral integrator FT gene showed different TFBS profiles. For example, the Arabidopsis FT gene was predicted to be regulated by CSD and MADS-box TFs. However, both the Brachypodium FT (BRADI1G48830) and barley FT gene (VRN3-H, MLOC_68576) appeared to be regulated by TFs in AP2, MADS and STK (storekeeper) families. The putative wheat FT1-A (Traes_7AS_EBD5F1F54) might be controlled by storekeeper (STK), MADS and AT hook (ATH) TF families, similar to FT1-B (Traes_7BS_581AA844D) and FT1-D (Traes_7DS_12C14942B, though only had one predicted TFBS each for ATH and STK family TFs). This difference of transcriptional regulation in FT genes may be supported by the view in [6]: the roles of FT-like genes appear to be highly conserved, but the TFs controlling their transcription vary during evolution, allowing transcription of FT-like genes in response to different conditions. Overall, the relative similarity of TFBS profiles in PH, VE and PI pathways are consistent with the conservation and divergence of these flowering pathways between the species [6, 8, 11].

Our GC content analysis was used as an indicator of promoters, because previous studies of regulatory sequences suggested that GC content has a significant difference between dicots and cereals, and methods of promoter identification based on DNA free energy profile were developed [32, 33]. Additionally, TFs from different families often prefer binding to regions with low or high GC content surrounding the core TFBS [72], thus it would be interesting to examine how the different GC content in the promoter regions of these genes might affect the binding environment of different TFs. In flower development, epigenetic regulation such as DNA methylation, histone modification, nucleosome positioning, and chromatin accessibility also plays an important role [21, 73–75]. Thus, it is possible that the different GC content in the promoter regions (particularly those surrounding predicted TFBSs) between Arabidopsis and cereal species might affect TF binding via epigenetic mechanisms.

The TFBS divergence relative to their corresponding coding sequences and introns was assessed using the Tajima test in VariScan [47, 48]. The Tajima’s D has previously been used to assess divergence patterns in the conifer EST (expressed sequence tag) data [76]. Our analysis suggests different binding preference of each TF family in each species, including MADS-box and CSD. And among these four species, the TFBSs diverged faster than their corresponding CDS, similar to the binding sites of Ste12 and Tec1 regulators in yeast [77]. More interestingly, introns might also diverge faster than CDS (but slower than TFBS). The potential function of introns in TFBSs and flowering regulation has gained growing attention [22, 23, 78, 79].

Despite its popularity for TFBS predictions, FIMO, like most motif search tools, may be prone to a high rate of false positives. There is definitely a need to further check the validity of FIMO-based predictions. We have tried out some recently-developed tools for comparative assessment with FIMO. In particular, BoBro 2.0 of Ma et al. [80] is an integreated toolkit aiming at improved control of false positive rate for the predicted TFBS motifs and higher prediction sensitivity through efficient handling of sequence variation in motifs. The preliminary results show that some TFBS motifs predicted by both FIMO and BoBro 2.0 have overlapping but not identical start/end positions while the majority of others are located at separate regions of the genome. Since BoBro 2.0 was developed initially for prokarotic genomes, further investigations are needed to make BoBro 2.0 or similar tools well adapted to the TFBS predictions for eukarotic genomes of higher plants such as those in our study.

Our FIMO-based TFBS motif search is based on known binding sites. When such knowledge is not available, de nevo motif discovery [81] has been suggested as an alternative approach to predicting TFBS motifs. In the de nevo motif discovery, multiple sequences are input to detect one or more candidate motifs. However, while the use of de novo motif search could have allowed us to find many more motifs, it would have been difficult to associate the motifs discovered de novo with known TFs. Without knowledge of their potential TFs, the predicted motifs would be of very limited value. Even though the de nevo motif search was not used in our study, it certainly needs to be explored in future studies.

Using the FIMO motif discovery tool in MEMPpE [28], we predicted a large number of putative TFBSs in the promoters of the genes related to the PH, VE and PI pathways in Arabidopsis, Brachypodium, wheat and barley. The quality of the predicted TFBSs was improved through cleaning both the inputs (promoter sequences and PWMs) and the FIMO outputs. The genes with similar TFBS numbers tend to be co-expressed in different tissues of each species. Based on our intraspecific and interspecific Tajima D-statistics [47, 48], TFBSs from different TF families showed different divergence within each species, and TFBSs are more divergent compared with CDS and introns. The TFBS numbers for major TF families were superimposed in the flowering pathways with PathVisio and WikiPathways [50, 51], to show the similarity and difference between these four species. The TFBSs and TF-targeted gene associations presented in our study can be investigated for their roles in photoperiod and vernalization responses in the genomes of four plant species, especially in the large, poorly-characterized genomes of two cereal crops, wheat and barley.