Search

Transcriptional regulation underlies most developmental programs and physiological responses to environmental changes in plants. Transcription factors (TFs) play a key role in the regulation of gene expression by binding specifically to short DNA sequences in the regulatory regions of genes: the TF binding sites (TFBSs). In recent years, several bioinformatic tools have been developed to detect TFBSs in candidate genes, either by de novo prediction or by directly mapping experimentally known TFBSs. However, most of these tools contain information for only a few species or require multi-step procedures, and are not always intuitive for non-experienced researchers. Here we present TFBS-Discovery Tool Hub (TDTHub), a web server for quick and intuitive studies of transcriptional regulation in plants. TDTHub uses pre-computed TFBSs in 40 plant species and allows the choice of two mapping algorithms, providing a higher versatility. Besides the main TFBS enrichment tool, TDTHub includes additional tools to assist in the analysis and visualization of data. In order to demonstrate the effectiveness of TDTHub, we analyzed the transcriptional regulation of the anthocyanin biosynthesis pathway. We also analyzed the transcriptional cascades in response to jasmonate and wounding in Arabidopsis and tomato (Solanum lycopersicum), respectively. In these studies, TDTHub helped to verify the most relevant TF nodes and to propose new ones with a prominent role in these pathways. TDTHub is available at http://acrab.cnb.csic.es/TDTHub/, and it will be periodically upgraded and expanded for new species and gene annotations.

Plant growth and adaptation to environmental fluctuations involve a tight control of cellular processes which, to a great extent, are mediated by changes at the transcriptional level. This regulation is exerted by transcription factors (TFs), a group of regulatory proteins that control gene expression by directly binding to the gene promoter regions via their cognate TF-binding sites (TFBS). The nature of TFBS defines the pattern of expression of the various plant loci, the precise combinatorial assembly of these elements being key in conferring plant's adaptation ability and in domestication. As such, TFs are main potential targets for biotechnological interventions, prompting in the last decade notable protein-DNA interaction efforts toward definition of their TFBS. Distinct methods based on in vivo or in vitro approaches defined the TFBS for many TFs, mainly in Arabidopsis, but comprehensive information on the transcriptional networks for many regulators is still lacking, especially in crops. In this chapter, detailed protocols for DAP-seq studies to unbiased identification of TFBS in potato are provided. This methodology relies on the affinity purification of genomic DNA-protein complexes in vitro, and high-throughput sequencing of the eluted DNA fragments. DAP-seq outperforms other in vitro DNA-motif definition strategies, such as protein-binding microarrays and SELEX-seq, since the protein of interest is directly bound to the genomic DNA extracted from plants yielding all the potential sites bound by the TF in the genome. Actually, data generated from DAP-seq experiments are highly similar to those out of ChIP-seq methods, but are generated much faster. We also provide a standard procedure to the analysis of the DAP-seq data, addressed to non-experienced users, that involves two consecutive steps: (1) processing of raw data (trimming, filtering, and read alignment) and (2) peak calling and identification of enriched motifs. This method allows identification of the binding profiles of dozens of TFs in crops, in a timely manner.

The tomato Mi-1 gene mediates plant resistance to whitefly Bemisia tabaci, nematodes, and aphids. Other genes are also required for this resistance, and a model of interaction between the proteins encoded by these genes was proposed. Microarray analyses were used previously to identify genes involved in plant resistance to pests or pathogens, but scarcely in resistance to insects. In the present work, the GeneChip™ Tomato Genome Array (Affymetrix®) was used to compare the transcriptional profiles of Motelle (bearing Mi-1) and Moneymaker (lacking Mi-1) cultivars, both before and after B. tabaci infestation. Ten transcripts were expressed at least twofold in uninfested Motelle than in Moneymaker, while other eight were expressed half or less. After whitefly infestation, differences between cultivars increased to 14 transcripts expressed more in Motelle than in Moneymaker and 14 transcripts less expressed. Half of these transcripts showed no differential expression before infestation. These results show the baseline differences in the tomato transcriptomic profile associated with the presence or absence of the Mi-1 gene and provide us with valuable information on candidate genes to intervene in either compatible or incompatible tomato-whitefly interactions.

Sequence-specific protein-DNA interactions mediate most regulatory processes underlying gene expression, such as transcriptional regulation by transcription factors (TFs) or chromatin organization. Current knowledge about DNA-binding specificities of TFs is based mostly on low- to medium-throughput methodologies that are time-consuming and often fail to identify DNA motifs recognized by a TF with lower affinity but retaining biological relevance. The use of protein-binding microarrays (PBMs) offers a high-throughput alternative for the identification of protein-DNA specificities. PBM consists in an array of pseudorandomized DNA sequences that are optimized to include all the possible 10- or 11-mer DNA sequences, allowing the determination of binding specificities of most eukaryotic TFs. PBMs that can be synthesized by several manufacturing companies as single-stranded DNA are converted into double-stranded in a simple primer extension reaction. The protein of interest fused to an epitope tag is then incubated onto the PBM, and specific DNA-protein complexes are revealed in a series of immunological reactions coupled to a fluorophore. After scanning and quantifying PBMs, specific DNA motifs recognized by the protein are identified with ready-to-use scripts, generating comprehensive but accessible information about the DNA-binding specificity of the protein. This chapter describes detailed procedures for preparation of double-stranded PBMs, incubation with recombinant protein, and detection of protein-DNA complexes. Finally, we outline some cues for evaluating the biological role of DNA motifs obtained in vitro.

Transcriptional regulation depends on the specificity of transcription factors (TFs) recognizing cis regulatory sequences in the promoters of target genes. Current knowledge about DNA-binding specificities of TFs is based mostly on low- to medium-throughput methodologies, revealing DNA motifs bound by a TF with high affinity. These strategies are time-consuming and often fail to identify DNA motifs recognized by a TF with lower affinity but retaining biological relevance. Here we report on the development of a protein-binding microarray (PBM11) containing all possible double-stranded 11-mers for the determination of DNA-binding specificities of TFs. The large number of sequences in the PBM11 allows accurate and high-throughput quantification of TF-binding sites, outperforming previous methods. We applied this tool to determine binding site specificities of two Arabidopsis TFs, MYC2 and ERF1, rendering the G-box and the GCC-box, respectively, as their highest-affinity binding sites. In addition, we identified variants of the G-box recognized by MYC2 with high and medium affinity, whereas ERF1 only recognized GCC variants with low affinity, indicating that ERF1 binding to DNA has stricter base requirements than MYC2. Analysis of transcriptomic data revealed that high- and medium-affinity binding sites have biological significance, probably representing relevant cis-acting elements in vivo. Comparison of promoter sequences with putative orthologs from closely related species demonstrated a high degree of conservation of all the identified DNA elements. The combination of PBM11, transcriptomic data and phylogenomic footprinting provides a straightforward method for the prediction of biologically active cis-elements, and thus for identification of in vivo DNA targets of TFs.