Identification Of Transcription Factor Binding Sites Research Articles

Comparison of different motif discovery algorithms

One of the most difficult challenges in molecular biology as well as computer science is finding patterns in DNA sequences. Identification of regulatory motifs is critical for understanding the gene expression. The essential concept in gene expression is that each a gene encodes the instructions for making a protein. The process of expression begins with the binding of several recognised protein factors. As transcription factors, they bind to enhancer and promoter sequences (Li and Li, 2019). Transcription is the first stage, which involves creating an RNA "copy" of a section of the DNA. This RNA sequence is read and interpreted to create a protein in the second stage of the process, known as translation. Gene expression is the combined result of these two actions. Numerous regulatory transcription factors (TFs), also known as Transcription Factor Binding Sites (TFBS), bind to certain DNA regions to control gene expression. In the past ten years, a significant new method for understanding transcription regulation networks has emerged: the computational identification of TFBS through the study of DNA sequence data (Ruzicka et al., 2017). Finding sequence motifs can be challenging since intergenic regions are extremely long and highly varied, while sequence motifs are small (approximately 6–12 bp). Sequence motifs are frequently repeated and conserved, and they have a fixed size. These patterns are critical for identifying Transcription Factor Binding Sites (TF-BSs), which aids in understanding the mechanisms governing gene expression 3. Motifs can be classified as planted, structured, gapped, sequence, network, and motifs (Hashim et al., 2019). An important issue in computational biology is the finding of weak motifs. It is challenging to solve because there are so many inconsistencies between the actual theme and its altered variants that false signals may mask the real ones. Further, it is challenging to identify and uncover regulatory elements using computer algorithms since they are typically brief and varied. The task of solving the theme finding problem is that of discovering overrepresented motifs as well as conserved motifs from the set of DNA sequences that are good candidates for becoming sites where transcription factors bind. Transcription factor is a protein that functions as a gene expression regulator, specifically regulating the start of the transcription process that produces mRNA using DNA as a template. The common sequence is called a motif. A "pattern" in a transcription factor's binding sites. Finding motifs will aid in the development of illness therapies and comprehension disease susceptibility (Mohanty and Mohanty, 2020). Many techniques for analysing gene function start with the finding of a DNA motif. Finding Transcription Factor Binding Sites (TFBSs), which aid in understanding the mechanisms for controlling gene expression, is a crucial part of motif discovery. The development of quick and precise motif discovery technologies has utilised a variety of algorithms over the years. These algorithms are typically categorised as probabilistic or consensus techniques, and many of them take a lot of time to run and are prone to get stuck in local optimums. Recently, solutions to these issues have been offered using both nature-inspired algorithms and a variety of combinatorial algorithms (Hashim et al., 2019).

Prediction and Analysis of Transcription Factor Binding Sites: Practical Examples and Case Studies Using R Programming.

Transcription factors (TFs) bind to specific regions of DNA known as transcription factor binding sites (TFBSs) and modulate gene expression by interacting with the transcriptional machinery. TFBSs are typically located upstream of target genes, within a few thousand base pairs of the transcription start site. The binding of TFs to TFBSs influences the recruitment of the transcriptional machinery, thereby regulating gene transcription in a precise and specific manner. This chapter provides practical examples and case studies demonstrating the extraction of upstream gene regions from the genome, identification of TFBSs using PWMEnrich R/Bioconductor package, interpretation of results, and preparation of publication-ready figures and tables. The EOMES promoter is used as a case study for single DNA sequence analysis, revealing potential regulation by the LHX9-FOXP1 complex during embryonic development. Additionally, an example is presented on how to investigate TFBSs in the upstream regions of a group of genes, using a case study of differentially expressed genes in response to human parainfluenza virus type 1 (HPIV1) infection and interferon-beta. Key regulators identified in this context include the STAT1:STAT2 heterodimer and interferon regulatory factor family proteins. The presented protocol is designed to be accessible to individuals with basic computer literacy. Understanding the interactions between TFs and TFBSs provides insights into the complex transcriptional regulatory networks that govern gene expression, with broad implications for several fields such as developmental biology, immunology, and disease research.

GENOME-WIDE ANALYSES OF CORE REGULATORY MODULE SHATTERING CASCADE GENES IN CANOLA (BRASSICA NAPUS L.)

Premature seed shattering in canola causes massive losses in yield by up to 50% in adverse climatic conditions. In the model plant Arabidopsis thaliana, which belongs to the same family as canola, the Brassicaceae, eight genes participate in a shattering cascade. Phylogenetic reconstruction, syntenic relationships, genomics loci, promoter sequences, and identification of transcription factor-binding sites (TFBSs) faced shattering cascade genes’ analysis. Among these, three genes, SHATTERPROOF1, SHATTERPROOF2, and FRUITFUL (SHP1, SHP2, FUL), belonged to a MADS-box family implicated in fruit dehiscence zone and valve margin constitute a core regulatory module. But, in Brassica, the exact number of genes involved in shattering remained obscure. Grouping BnSHP1-N, BnSHP2-N, and BnFUL-N into their respective clades was according to phylogenetic reconstruction of core regulatory modules (SHP1, SHP2, and FUL) and from other species homologs. The eight shattering cascade genes showed no conservation, indicating their involvement in crushing through separate pathways. The increased number of homologs/paralogs in Brassica was due to occurrences of genome duplication or a triplication event during evolution. Exonization and intronization could be responsible for a variable number and size of the exons and introns in gene structures. Comparative genome synteny analysis of SHP1, SHP2, and FUL revealed correlation and evolutionary insights into gene region relationships in all Brassicaceae. Study results provided basic information on cloning, phylogenetic reconstruction, genomics loci, and identifying transcription factor-binding sites (TFBSs) of core regulatory module genes that might be helpful for developing shattering-resistant genome-edited plants to prevent future yield losses in canola.

BLSSpeller to discover novel regulatory motifs in maize.

With the decreasing cost of sequencing and availability of larger numbers of sequenced genomes, comparative genomics is becoming increasingly attractive to complement experimental techniques for the task of transcription factor (TF) binding site identification. In this study, we redesigned BLSSpeller, a motif discovery algorithm, to cope with larger sequence datasets. BLSSpeller was used to identify novel motifs in Zea mays in a comparative genomics setting with 16 monocot lineages. We discovered 61 motifs of which 20 matched previously described motif models in Arabidopsis. In addition, novel, yet uncharacterized motifs were detected, several of which are supported by available sequence-based and/or functional data. Instances of the predicted motifs were enriched around transcription start sites and contained signatures of selection. Moreover, the enrichment of the predicted motif instances in open chromatin and TF binding sites indicates their functionality, supported by the fact that genes carrying instances of these motifs were often found to be co-expressed and/or enriched in similar GO functions. Overall, our study unveiled several novel candidate motifs that might help our understanding of the genotype to phenotype association in crops.

DAP-Seq Identification of Transcription Factor-Binding Sites in Potato.

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.

Transcription Factor Binding Affinities and DNA Shape Readout.

SummaryAn essential event in gene regulation is the binding of a transcription factor (TF) to its target DNA. Models considering the interactions between the TF and the DNA geometry proved to be successful approaches to describe this binding event, while conserving data interpretability. However, a direct characterization of the DNA shape contribution to binding is still missing due to the lack of accurate and large-scale binding affinity data. Here, we use a binding assay we recently established to measure with high sensitivity the binding specificities of 13 Drosophila TFs, including dinucleotide dependencies to capture non-independent amino acid-base interactions. Correlating the binding affinities with all DNA shape features, we find that shape readout is widely used by these factors. A shape readout/TF-DNA complex structure analysis validates our approach while providing biological insights such as positively charged or highly polar amino acids often contact nucleotides that exhibit strong shape readout.

The transcription factor Leu3 shows differential binding behavior in response to changing leucine availability.

ABSTRACTThe main transcriptional regulator of leucine biosynthesis in the yeast Saccharomyces cerevisiae is the transcription factor Leu3. It has previously been reported that Leu3 always binds to its target genes, but requires activation to induce their expression. In a recent large-scale study of high-resolution transcription factor binding site identification, we showed that Leu3 has divergent binding sites in different cultivation conditions, thereby questioning the results of earlier studies. Here, we present a follow-up study using chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate the influence of leucine supplementation on Leu3 binding activity and strength. With this new data set we are able to show that Leu3 exhibits changes in binding activity in response to changing levels of leucine availability.

Discovering Transcription Factor Noncoding RNA Targets Using ChIP-Seq Analysis.

Next generation sequencing enables large-scale analysis of mRNA expression (RNA-seq), genome variance (whole genome or exome), and transcription factor binding (ChIP-seq). Here we describe a method that allows the identification of transcription factor-binding sites in the vicinity of nonprotein-coding genes.

Identification of Transcription Factor-Binding Sites in the Mouse FOXO1 Promoter.

One critical determinant of levels of gene expression is binding of transcription factors to cognate DNA sequences in promoter and enhancer regions of target genes. Transcription factors are DNA-binding proteins to which transcriptional co-regulators are bound, ultimately resulting in histone modifications that change chromatin structure to regulate transcription. Examples of transcription factors include hormone-activated transcription factors such as the glucocorticoid receptor, transcription factors regulated by cell surface receptors such as FOXO1 and Smad2/Smad3, and many others. Promoter regions typically contain multiple, diverse transcription factor-binding sites. Binding sites for cell-type-specific transcription factors involved in cell fate determination such as Runx2, MyoD, or myogenin are frequently observed. Promoter regions are located within ~2kb upstream of the transcriptional start site, whereas enhancers may be located at some distance from promoter sequences and exert long-range effects. Here, we will discuss classical and emerging technologies by which one can understand the role of binding of specific transcription factors in regulation of transcription of FOXO genes.

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, but inappropriate initialization of EM has yielded poor performance or running time scalability under a given data set. In this study, we used a sequential integration approach that defined the final solution as the set of solutions acquired from solving objectives in a cascade manner to integrate the fuzzy C-means and the EM approaches to DNA motif discovery. The new method is explained in detail and tested on the chromatin immunoprecipitation sequencing (ChIP-seq) data sets for different transcription factors (TFs) with various motif patterns. The proposed algorithm also suggests an efficient process for analyzing motif similarity to known motifs as well as finding a target motif. A comparison of results with those of the well-known motif-finding tool, MEME-ChIP, shows the advantages of our proposed framework over this existing tool. Experimental results show that we were able to find the true motifs for all TFs, and that the motifs found by our proposed algorithm were more similar to JASPAR-known motifs for the STAT1, GATA1, and JUN TFs than those found by MEME-ChIP.

Genome-Wide Identification of Transcription Factor-Binding Sites in Quiescent Adult Neural Stem Cells.

Transcription factors bind to specific DNA sequences and control the transcription rate of nearby genes in the genome. This activation or repression of gene expression is further potentiated by epigenetic modifications of histones with active and silent marks, respectively. Resident adult stem cells in the hematopoietic system, skin, and brain exist in a non-proliferative quiescent resting state. When quiescent stem cells become activated and transition to dividing progenitors and distinct cell types, they can replenish and repair tissue. Thus, determination of the position of transcription factor binding and histone epigenetic modification on the chromatin is an essential step toward understanding the gene regulation of quiescent and proliferative adult stem cells for potential applications in regenerative medicine. Genome-wide transcription factor occupancy and histone modifications on the genome can be obtained by assessing DNA-protein interaction through next-generation chromatin immunoprecipitation sequencing technology (ChIP-seq). This chapter outlines the protocol to perform, analyze, and validate ChIP-seq experiments that can be used to identify protein-DNA interactions and histone marks on the chromatin. The methods described here are applicable to quiescent and proliferative neural stem cells, and a wide range of other cellular systems.

Structural modelling and molecular dynamics of a multi-stress responsive WRKY TF-DNA complex towards elucidating its role in stress signalling mechanisms in chickpea

Chickpea is a premier food legume crop with high nutritional quality and attains prime importance in the current era of 795 million people being undernourished worldwide. Chickpea production encounters setbacks due to various stresses and understanding the role of key transcription factors (TFs) involved in multiple stresses becomes inevitable. We have recently identified a multi-stress responsive WRKY TF in chickpea. The present study was conducted to predict the structure of WRKY TF to identify the DNA-interacting residues and decipher DNA-protein interactions. Comparative modelling approach produced 3D model of the WRKY TF with good stereochemistry, local/global quality and further revealed W19, R20, K21, and Y22 motifs within a vicinity of 5 Å to the DNA amongst R18, G23, Q24, K25, Y36, Y37, R38 and K47 and these positions were equivalent to the 2LEX WRKY domain of Arabidopsis. Molecular simulations analysis of reference protein -PDB ID 2LEX, along with Car-WRKY TF modelled structure with the DNA coordinates derived from PDB ID 2LEX and docked using HADDOCK were executed. Root Mean Square (RMS) Deviation and RMS Fluctuation values yielded consistently stable trajectories over 50 ns simulation. Strengthening the obtained results, neither radius of gyration, distance and total energy showed any signs of DNA-WRKY complex falling apart nor any significant dissociation event over 50 ns run. Therefore, the study provides first insights into the structural properties of multi-stress responsive WRKY TF-DNA complex in chickpea, enabling genome wide identification of TF binding sites and thereby deciphers their gene regulatory networks.

Knowledge-based three-body potential for transcription factor binding site prediction.

A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction.

ChIP-Seq Data Analysis to Define Transcriptional Regulatory Networks.

The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites. This chapter provides a survey on how data of this kind are to be processed and integrated with expression and other types of data to infer transcriptional regulatory rules and codes.

Data in support of FSH induction of IRS-2 in human granulosa cells: Mapping the transcription factor binding sites in human IRS-2 promoter.

Insulin receptor substrate-2 (IRS-2) plays critical role in the regulation of various metabolic processes by insulin and IGF-1. The defects in its expression and/or function are linked to diseases like polycystic ovary syndrome (PCOS), insulin resistance and cancer. To predict the transcription factors (TFs) responsible for the regulation of human IRS-2 gene expression, the transcription factor binding sites (TFBS) and the corresponding TFs were investigated by analysis of IRS-2 promoter sequence using MatInspector Genomatix software (Cartharius et al., 2005 [1]). The ibid data is part of author׳s publication (Anjali et al., 2015 [2]) that explains Follicle stimulating hormone (FSH) mediated IRS-2 promoter activation in human granulosa cells and its importance in the pathophysiology of PCOS. Further analysis was carried out for binary interactions of TF regulatory genes in IRS-2 network using Cytoscape software tool and R-code. In this manuscript, we describe the methodology used for the identification of TFBSs in human IRS-2 promoter region and provide details on experimental procedures, analysis method, validation of data and also the raw files. The purpose of this article is to provide the data on all TFBSs in the promoter region of human IRS-2 gene as it has the potential for prediction of the regulation of IRS-2 gene in normal or diseased cells from patients with metabolic disorders and cancer.

Conjugated bilirubin affects cytokine profiles in hepatitis A virus infection by modulating function of signal transducer and activator of transcription factors.

Hepatitis A virus (HAV) infection is the major cause of acute liver failure in paediatric patients. The clinical spectrum of infection is variable, and liver injury is determined by altered hepatic enzyme function and bilirubin concentration. We recently reported differences in cytokine profiles between distinct HAV-induced clinical courses, and bilirubin has been recognized as a potential immune-modulator. However, how bilirubin may affect cytokine profiles underlying the variability in the course of infection has not been determined. Herein, we used a transcription factor (TF) binding site identification approach to retrospectively analyse cytokine expression in HAV-infected children and to predict the entire set of TFs associated with the expression of specific cytokine profiles. The results suggested that modulation of the activity of signal transducers and activators of transcription proteins (STATs) may play a central role during HAV infection. This led us to compare the degree of STAT phosphorylation in peripheral blood lymphoid cells (PBLCs) from paediatric patients with distinct levels of conjugated bilirubin (CB). Low CB levels in sera were associated with increased STAT-1 and STAT-5 phosphorylation. A positive correlation was observed between the serum interleukin-6 (IL-6) content and CB values, whereas higher levels of CB correlated with reduced serum IL-8 values and with a reduction in the proportion of PBLCs positive for STAT-5 phosphorylation. When CB was used to stimulate patients' PBLCs in vitro, the levels of IL-6 and tumour necrosis factor-α were increased. The data showed that bilirubin plays a role in STAT function and affects cytokine profile expression during HAV infection.

RUNX1 positively regulates a cell adhesion and migration program in murine hemogenic endothelium prior to blood emergence

AbstractDuring ontogeny, the transcription factor RUNX1 governs the emergence of definitive hematopoietic cells from specialized endothelial cells called hemogenic endothelium (HE). The ultimate consequence of this endothelial-to-hematopoietic transition is the concomitant activation of the hematopoietic program and downregulation of the endothelial program. However, due to the rare and transient nature of the HE, little is known about the initial role of RUNX1 within this population. We, therefore, developed and implemented a highly sensitive DNA adenine methyltransferase identification-based methodology, including a novel data analysis pipeline, to map early RUNX1 transcriptional targets in HE cells. This novel transcription factor binding site identification protocol should be widely applicable to other low abundance cell types and factors. Integration of the RUNX1 binding profile with gene expression data revealed an unexpected early role for RUNX1 as a positive regulator of cell adhesion- and migration-associated genes within the HE. This suggests that RUNX1 orchestrates HE cell positioning and integration prior to the release of hematopoietic cells. Overall, our genome-wide analysis of the RUNX1 binding and transcriptional profile in the HE provides a novel comprehensive resource of target genes that will facilitate the precise dissection of the role of RUNX1 in early blood development.

Inference of transcriptional networks in Arabidopsis through conserved noncoding sequence analysis.

Transcriptional regulation plays an important role in establishing gene expression profiles during development or in response to (a)biotic stimuli. Transcription factor binding sites (TFBSs) are the functional elements that determine transcriptional activity, and the identification of individual TFBS in genome sequences is a major goal to inferring regulatory networks. We have developed a phylogenetic footprinting approach for the identification of conserved noncoding sequences (CNSs) across 12 dicot plants. Whereas both alignment and non-alignment-based techniques were applied to identify functional motifs in a multispecies context, our method accounts for incomplete motif conservation as well as high sequence divergence between related species. We identified 69,361 footprints associated with 17,895 genes. Through the integration of known TFBS obtained from the literature and experimental studies, we used the CNSs to compile a gene regulatory network in Arabidopsis thaliana containing 40,758 interactions, of which two-thirds act through binding events located in DNase I hypersensitive sites. This network shows significant enrichment toward in vivo targets of known regulators, and its overall quality was confirmed using five different biological validation metrics. Finally, through the integration of detailed expression and function information, we demonstrate how static CNSs can be converted into condition-dependent regulatory networks, offering opportunities for regulatory gene annotation.

AthaMap web tools for the analysis of transcriptional and posttranscriptional regulation of gene expression in Arabidopsis thaliana.

The AthaMap database provides a map of verified and predicted transcription factor (TF) and small RNA-binding sites for the A. thaliana genome. The database can be used for bioinformatic predictions of putative regulatory sites. Several online web tools are available that address specific questions. Starting with the identification of transcription factor-binding sites (TFBS) in any gene of interest, colocalizing TFBS can be identified as well as common TFBS in a set of user-provided genes. Furthermore, genes can be identified that are potentially targeted by specific transcription factors or small inhibitory RNAs. This chapter provides detailed information on how each AthaMap web tool can be used online. Examples on how this database is used to address questions in circadian and diurnal regulation are given. Furthermore, complementary databases and databases that go beyond questions addressed with AthaMap are discussed.

Identifying Functional Transcription Factor Binding Sites in Yeast by Considering Their Positional Preference in the Promoters

Transcription factor binding site (TFBS) identification plays an important role in deciphering gene regulatory codes. With comprehensive knowledge of TFBSs, one can understand molecular mechanisms of gene regulation. In the recent decades, various computational approaches have been proposed to predict TFBSs in the genome. The TFBS dataset of a TF generated by each algorithm is a ranked list of predicted TFBSs of that TF, where top ranked TFBSs are statistically significant ones. However, whether these statistically significant TFBSs are functional (i.e. biologically relevant) is still unknown. Here we develop a post-processor, called the functional propensity calculator (FPC), to assign a functional propensity to each TFBS in the existing computationally predicted TFBS datasets. It is known that functional TFBSs reveal strong positional preference towards the transcriptional start site (TSS). This motivates us to take TFBS position relative to the TSS as the key idea in building our FPC. Based on our calculated functional propensities, the TFBSs of a TF in the original TFBS dataset could be reordered, where top ranked TFBSs are now the ones with high functional propensities. To validate the biological significance of our results, we perform three published statistical tests to assess the enrichment of Gene Ontology (GO) terms, the enrichment of physical protein-protein interactions, and the tendency of being co-expressed. The top ranked TFBSs in our reordered TFBS dataset outperform the top ranked TFBSs in the original TFBS dataset, justifying the effectiveness of our post-processor in extracting functional TFBSs from the original TFBS dataset. More importantly, assigning functional propensities to putative TFBSs enables biologists to easily identify which TFBSs in the promoter of interest are likely to be biologically relevant and are good candidates to do further detailed experimental investigation. The FPC is implemented as a web tool at http://santiago.ee.ncku.edu.tw/FPC/.