Specific Transcription Factor Binding Research Articles

HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis.

We present a major update of the HOCOMOCO collection that consists of patterns describing DNA binding specificities for human and mouse transcription factors. In this release, we profited from a nearly doubled volume of published in vivo experiments on transcription factor (TF) binding to expand the repertoire of binding models, replace low-quality models previously based on in vitro data only and cover more than a hundred TFs with previously unknown binding specificities. This was achieved by systematic motif discovery from more than five thousand ChIP-Seq experiments uniformly processed within the BioUML framework with several ChIP-Seq peak calling tools and aggregated in the GTRD database. HOCOMOCO v11 contains binding models for 453 mouse and 680 human transcription factors and includes 1302 mononucleotide and 576 dinucleotide position weight matrices, which describe primary binding preferences of each transcription factor and reliable alternative binding specificities. An interactive interface and bulk downloads are available on the web: http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco11. In this release, we complement HOCOMOCO by MoLoTool (Motif Location Toolbox, http://molotool.autosome.ru) that applies HOCOMOCO models for visualization of binding sites in short DNA sequences.

Promoter architecture and transcriptional regulation of musculoskeletal embryonic nuclear protein 1b (mustn1b) gene in zebrafish.

Mustn1 is a specific musculoskeletal protein that plays a critical role in myogenesis and chondrogenesis in vertebrates. Whole-mount in situ hybridization revealed that mustn1b mRNAs are specifically expressed in skeletal and cardiac muscles in Zebrafish embryos. However, the precise function and the regulatory elements required for its muscle-specific expression are largely unknown. The purpose of this study was to explore and uncover the target genomic regions that regulate mustn1b gene expression by in vivo functional characterization of the mustn1b promoter. We report here stable expression analyses of eGFP from fluorescent transgenic reporter Zebrafish line containing a 0.8kb_mustn1b-Tol2-eGFP construct. eGFP expression was specifically found in the skeletal and cardiac muscle tissues. We show that reporter Zebrafish lines generated replicate the endogenous mustn1b expression pattern in early Zebrafish embryos. Specific site directed-mutagenesis analysis revealed that promoter activity resides in two annotated genomic regulatory regions, each one corresponding to a specific functional transcription factor binding site. Our data indicate that mustn1b is specifically expressed in skeletal and cardiac muscle tissues and its muscle specificity is controlled by the 0.2-kb promoter and flanking sequences and in vivo regulated by the action of two sequence-specific families of transcription factors. Developmental Dynamics 246:992-1000, 2017. © 2017 Wiley Periodicals, Inc.

Identification and characterization of a FOXA2-regulated transcriptional enhancer at a type 2 diabetes intronic locus that controls GCKR expression in liver cells

BackgroundGenome-wide association studies (GWAS) have identified more than 100 genetic loci associated with type 2 diabetes (T2D). However, the underlying biological mechanisms for many of these associations remain unknown. GWAS signals close to the glucokinase regulatory protein gene (GCKR) have been reported for lipid and glucose metabolism traits and the risk of T2D. We investigated the regulatory function of an intronic locus at GCKR represented by the lead single nucleotide polymorphism (SNP) rs780094.MethodsWe used ENCODE project histone modification and transcription factor binding data to determine the regulatory features of a GCKR intronic locus formed by the high linkage disequilibrium rs780094(C/T), rs780095(G/A), and rs780096(G/C) SNPs. Characterization of the transcriptional activity of this region was assessed by luciferase reporter assays in HepG2 cells and mouse primary hepatocytes. ChIP-qPCR was used to determine the levels of haplotype specific transcription factor binding and histone marks. A CRISPR-dCas9 transcriptional activator system and qPCR were used to activate the locus and measure GCKR expression, respectively. Differential haplotype expression was measured from human liver biopsies.ResultsThe ENCODE data suggest the existence of a liver-specific intragenic enhancer at the locus represented by s780094. We observed that FOXA2 increased the transcriptional activity of this region in a haplotype specific way (CGG > TAC; rs780094, rs780095, and rs780096). In addition, the CGG haplotype showed higher binding to FOXA2 and higher levels of the H3K27Ac histone mark. The epigenetic activation of this locus increased the expression of endogenous GCKR in HepG2 cells, confirming that GCKR is the direct target gene of the enhancer. Finally, we confirmed that the CGG haplotype exhibits higher levels of transcription in human liver.ConclusionsOur results demonstrate the existence of a liver-specific FOXA2-regulated transcriptional enhancer at an intronic T2D locus represented by rs780094, rs780095, and rs780096 SNPs that increases GCKR expression. Differential haplotype regulation suggests the existence of cis regulatory effects that may contribute to the associated traits at this locus.

Abstract 1306: Two distinct regulatory mechanisms underlie estrogen receptor negative breast cancer susceptibility at the 2p23.2 locus

Abstract Genome Wide Association Studies (GWAS) are designed to identify single nucleotide polymorphisms (SNPs) mapping to genomic regions associated with a particular trait or disease. Currently, 19 genomic loci associated with risk for estrogen receptor negative breast cancer have been identified. However, for most of these loci the mechanisms driving susceptibility have remained elusive. The 2p23.2 locus, defined by SNP rs67073037 (p = 4.76 x 10-9), contains 79 SNPs exhibiting genome-wide significant associations as well as four candidate genes: SPDYA, TRMT61B, WDR43, and FAM179A. Post-GWAS functional analysis revealed two candidate causal regulatory elements: an enhancer-like element defined by SNP rs44017214 located in the first intron of the WDR43 gene; and a segment in the 3’-UTR region of WDR43 defined by SNPs rs11680458 and rs11331880 which are predicted to have allele specific binding of non-coding microRNAs miR-548 and miR-376, respectively. We validated allele specific transcription factor binding to rs44017214 using competitive binding of USF1/USF2 to the risk allele in both MCF10A and CAL51 cell lines in Electrophoretic Mobility Shift Assays (EMSA). Additionally, rs44017214 was removed using CRISPR-cas9, and expression of neighboring genes was evaluated by qRT-PCR. No change in expression was found for any gene in a 2 Mb region surrounding the SNP, with the exception of PLB1. This suggests that the enhancer element in the intron of WDR43 has PLB1 as its target. To assess the miRNA binding site impact on WDR43 expression the pMIR-REPORT Luciferase allele-specific vector was co-transfected with miR-141/200, miR548, or miR-376 in MCF10A cells. The rs1131880 allele C in the WDR43 3’-UTR lead to the suppression of luciferase expression by miR-376 compared to the G allele. This study reveals two independent regulatory mechanisms within the 2p23.2 locus; (i) modulation of enhancer activity by allele specific binding of transcription factors USF1/USF2 to rs4401714, and (ii) decreasing the expression of WDR43 through the formation of a miR-376 binding site at the 3’-UTR of the WDR43 gene. Elucidating the functional significance of these common variants in the novel susceptibility loci 2p23.2 furthers our knowledge of the etiology of estrogen receptor negative breast cancer. Citation Format: Carly M. Harro, Gustavo Mendoza-Fandino, Nicholas T. Woods, Xueli Li, Fergus J. Couch, Alvaro N. Monteiro. Two distinct regulatory mechanisms underlie estrogen receptor negative breast cancer susceptibility at the 2p23.2 locus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1306. doi:10.1158/1538-7445.AM2017-1306

Impact of cytosine methylation on DNA binding specificities of human transcription factors.

The majority of CpG dinucleotides in the human genome are methylated at cytosine bases. However, active gene regulatory elements are generally hypomethylated relative to their flanking regions, and the binding of some transcription factors (TFs) is diminished by methylation of their target sequences. By analysis of 542 human TFs with methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment), we found that there are also many TFs that prefer CpG-methylated sequences. Most of these are in the extended homeodomain family. Structural analysis showed that homeodomain specificity for methylcytosine depends on direct hydrophobic interactions with the methylcytosine 5-methyl group. This study provides a systematic examination of the effect of an epigenetic DNA modification on human TF binding specificity and reveals that many developmentally important proteins display preference for mCpG-containing sequences.

BEESEM: estimation of binding energy models using HT-SELEX data.

Characterizing the binding specificities of transcription factors (TFs) is crucial to the study of gene expression regulation. Recently developed high-throughput experimental methods, including protein binding microarrays (PBM) and high-throughput SELEX (HT-SELEX), have enabled rapid measurements of the specificities for hundreds of TFs. However, few studies have developed efficient algorithms for estimating binding motifs based on HT-SELEX data. Also the simple method of constructing a position weight matrix (PWM) by comparing the frequency of the preferred sequence with single-nucleotide variants has the risk of generating motifs with higher information content than the true binding specificity. We developed an algorithm called BEESEM that builds on a comprehensive biophysical model of protein-DNA interactions, which is trained using the expectation maximization method. BEESEM is capable of selecting the optimal motif length and calculating the confidence intervals of estimated parameters. By comparing BEESEM with the published motifs estimated using the same HT-SELEX data, we demonstrate that BEESEM provides significant improvements. We also evaluate several motif discovery algorithms on independent PBM and ChIP-seq data. BEESEM provides significantly better fits to in vitro data, but its performance is similar to some other methods on in vivo data under the criterion of the area under the receiver operating characteristic curve (AUROC). This highlights the limitations of the purely rank-based AUROC criterion. Using quantitative binding data to assess models, however, demonstrates that BEESEM improves on prior models. Freely available on the web at http://stormo.wustl.edu/resources.html . stormo@wustl.edu. Supplementary data are available at Bioinformatics online.

Recurrent rewiring and emergence of RNA regulatory networks

Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

Discovery and validation of information theory-based transcription factor and cofactor binding site motifs.

Data from ChIP-seq experiments can derive the genome-wide binding specificities of transcription factors (TFs) and other regulatory proteins. We analyzed 765 ENCODE ChIP-seq peak datasets of 207 human TFs with a novel motif discovery pipeline based on recursive, thresholded entropy minimization. This approach, while obviating the need to compensate for skewed nucleotide composition, distinguishes true binding motifs from noise, quantifies the strengths of individual binding sites based on computed affinity and detects adjacent cofactor binding sites that coordinate with the targets of primary, immunoprecipitated TFs. We obtained contiguous and bipartite information theory-based position weight matrices (iPWMs) for 93 sequence-specific TFs, discovered 23 cofactor motifs for 127 TFs and revealed six high-confidence novel motifs. The reliability and accuracy of these iPWMs were determined via four independent validation methods, including the detection of experimentally proven binding sites, explanation of effects of characterized SNPs, comparison with previously published motifs and statistical analyses. We also predict previously unreported TF coregulatory interactions (e.g. TF complexes). These iPWMs constitute a powerful tool for predicting the effects of sequence variants in known binding sites, performing mutation analysis on regulatory SNPs and predicting previously unrecognized binding sites and target genes.

Transcription Factor-DNA Binding Motifs in Saccharomyces cerevisiae: Tools and Resources.

The DNA binding specificity of transcription factors (TFs) is typically represented in the form of a position weight matrix (PWM), also known as a DNA motif. A PWM is a matrix that specifies, for each position in the DNA binding site of a TF, the "weight" or contribution of each possible nucleotide. DNA motifs can be derived from various types of TF-DNA binding data, from small collections of known TF binding sites to large data sets generated using high-throughput technologies. One drawback of this simple model of DNA binding specificity is that it makes the implicit assumption that individual base pairs within a TF binding site contribute independently to the TF-DNA binding affinity. Although this assumption does not always hold, PWM models have been shown to provide reasonable approximations to the DNA binding specificity, and they are still widely used in practice. DNA motifs are currently available for more than 150 Saccharomyces cerevisiae TFs. Here, we briefly describe how these models are built, we provide information on databases containing DNA motifs for S. cerevisiae TFs, and we introduce guidelines on how to interpret the motifs and use them in practice to generate hypotheses about transcriptional regulatory regions.

Computational inference of a genomic pluripotency signature in human and mouse stem cells.

Recent analyses of next-generation sequencing datasets have shown that cell-specific regulatory elements in stem cells are marked with distinguishable patterns of transcription factor (TF) binding and epigenetic marks. For example, we recently demonstrated that promoters of cell-specific genes are covered with expanded trimethylation of histone H3 at lysine 4 (H3K4me3) marks (i.e., broad H3K4me3 domains). Moreover, binding of specific TFs, such as OCT4, NANOG, and SOX2, have been shown to play a critical role in maintaining the pluripotency of stem cells. Despite these observations, a systematic exploration of genomic and epigenomic features of stem-cell-specific gene promoters has not been conducted. Advanced machine-learning models can capture distinguishable genomic and epigenomic characteristics of stem-cell-specific promoters by taking advantage of the wealth of publicly available datasets. Here, we propose a three-step framework to discover novel data characteristics of high-throughput next generation sequencing datasets that distinguish pluripotency genes in human and mouse embryonic stem cells (ESCs). Our framework involves: i) feature extraction to identify novel features of genomic datasets; ii) feature selection using a logistic regression model combined with the Least Absolute Shrinkage and Selection Operator (LASSO) method to find the most critical datasets and features; and iii) cross validation with features selected using LASSO method to assess the predictive power of selected data features in distinguishing pluripotency genes. We show that specific epigenetic marks, and specific features of these marks, are enriched at pluripotency gene promoters. Moreover, we also assess both the individual and combined effect of TF binding, epigenetic mark deposition, gene expression datasets for marking pluripotency genes. Our findings are consistent with the existence of a conserved, complex and integrative genomic signature in ESCs that can be exploited to flag important candidate pluripotency genes. They also validate our computational framework for fostering a deeper understanding of genomic datasets in stem cells, in the future, could be extended to study cell-type-specific genomic landscapes in other cell types.Reviewers: This article was reviewed by Zoltan Gaspari and Piotr Zielenkiewicz.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0148-z) contains supplementary material, which is available to authorized users.

The human olfactory transcriptome.

BackgroundOlfaction is a versatile sensory mechanism for detecting thousands of volatile odorants. Although molecular basis of odorant signaling is relatively well understood considerable gaps remain in the complete charting of all relevant gene products. To address this challenge, we applied RNAseq to four well-characterized human olfactory epithelial samples and compared the results to novel and published mouse olfactory epithelium as well as 16 human control tissues.ResultsWe identified 194 non-olfactory receptor (OR) genes that are overexpressed in human olfactory tissues vs. controls. The highest overexpression is seen for lipocalins and bactericidal/permeability-increasing (BPI)-fold proteins, which in other species include secreted odorant carriers. Mouse-human discordance in orthologous lipocalin expression suggests different mammalian evolutionary paths in this family.Of the overexpressed genes 36 have documented olfactory function while for 158 there is little or no previous such functional evidence. The latter group includes GPCRs, neuropeptides, solute carriers, transcription factors and biotransformation enzymes. Many of them may be indirectly implicated in sensory function, and ~70 % are over expressed also in mouse olfactory epithelium, corroborating their olfactory role.Nearly 90 % of the intact OR repertoire, and ~60 % of the OR pseudogenes are expressed in the olfactory epithelium, with the latter showing a 3-fold lower expression. ORs transcription levels show a 1000-fold inter-paralog variation, as well as significant inter-individual differences. We assembled 160 transcripts representing 100 intact OR genes. These include 1–4 short 5’ non-coding exons with considerable alternative splicing and long last exons that contain the coding region and 3’ untranslated region of highly variable length. Notably, we identified 10 ORs with an intact open reading frame but with seemingly non-functional transcripts, suggesting a yet unreported OR pseudogenization mechanism. Analysis of the OR upstream regions indicated an enrichment of the homeobox family transcription factor binding sites and a consensus localization of a specific transcription factor binding site subfamily (Olf/EBF).ConclusionsWe provide an overview of expression levels of ORs and auxiliary genes in human olfactory epithelium. This forms a transcriptomic view of the entire OR repertoire, and reveals a large number of over-expressed uncharacterized human non-receptor genes, providing a platform for future discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2960-3) contains supplementary material, which is available to authorized users.

Genomic analysis identified a potential novel molecular mechanism for high-altitude adaptation in sheep at the Himalayas.

Sheep has successfully adapted to the extreme high-altitude Himalayan region. To identify genes underlying such adaptation, we genotyped genome-wide single nucleotide polymorphisms (SNPs) of four major sheep breeds living at different altitudes in Nepal and downloaded SNP array data from additional Asian and Middle East breeds. Using a di value-based genomic comparison between four high-altitude and eight lowland Asian breeds, we discovered the most differentiated variants at the locus of FGF-7 (Keratinocyte growth factor-7), which was previously reported as a good protective candidate for pulmonary injuries. We further found a SNP upstream of FGF-7 that appears to contribute to the divergence signature. First, the SNP occurred at an extremely conserved site. Second, the SNP showed an increasing allele frequency with the elevated altitude in Nepalese sheep. Third, the electrophoretic mobility shift assays (EMSA) analysis using human lung cancer cells revealed the allele-specific DNA-protein interactions. We thus hypothesized that FGF-7 gene potentially enhances lung function by regulating its expression level in high-altitude sheep through altering its binding of specific transcription factors. Especially, FGF-7 gene was not implicated in previous studies of other high-altitude species, suggesting a potential novel adaptive mechanism to high altitude in sheep at the Himalayas.

Heterogeneity of transcription factor binding specificity models within and across cell lines.

Complex gene expression patterns are mediated by the binding of transcription factors (TFs) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TF's DNA binding interaction partners, motivating genomic context-based models of TF occupancy. However, approaches thus far have assumed a uniform TF binding model to explain genome-wide cell-type–specific binding sites. Therefore, the cell type heterogeneity of TF occupancy models, as well as the extent to which binding rules underlying a TF's occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble-based approach (TRISECT) to identify the heterogeneous binding rules for cell-type–specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-seq data in four to 12 different cell types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy. Importantly, many of the binding rules derived from individual cell types are shared across cell types and reveal distinct yet functionally coherent putative target genes in different cell types. Closer inspection of the predicted cell-type–specific interaction partners provides insights into the context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising the interaction partners within a cell type, many of which nevertheless transcend cell types. Notably, the putative targets of shared binding rules in different cell types, while distinct, exhibit significant functional coherence.

Structure-based prediction of transcription factor binding specificity using an integrative energy function.

Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and π interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals.Contact: jguo4@uncc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Survey of variation in human transcription factors reveals prevalent DNA binding changes.

Sequencing of exomes and genomes has revealed abundant genetic variation affecting the coding sequences of human transcription factors (TFs), but the consequences of such variation remain largely unexplored. We developed a computational, structure-based approach to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activity across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases. We found 77 variants in 28 genes that affect DNA binding affinity or specificity and identified thousands of rare alleles likely to alter the DNA binding activity of human sequence-specific TFs. Our results suggest that most individuals have unique repertoires of TF DNA binding activities, which may contribute to phenotypic variation.

Variation and transcription factor binding

Protein Evolution Little is known about the phenotypic and functional effects of genetic variants that result in amino acid changes within functional proteins. Barrera et al. investigated whether amino acid variants changed the DNA binding specificity or affinity of transcription factors. Predictive analyses identified changes in the proteins, and protein-binding microarrays verified changes that affected transcription factor function, including those associated with disease. Thus, within-human protein sequence variation can affect transcriptional regulatory networks, which, depending on the genetic variant, may confer robustness and buffer against amino acid changes and could explain phenotypic variation among individuals. Science , this issue p. [1450][1] [1]: /lookup/doi/10.1126/science.aad2257

Transcription factor motif quality assessment requires systematic comparative analysis.

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

The orientation of transcription factor binding site motifs in gene promoter regions: does it matter?

BackgroundGene expression is to large degree regulated by the specific binding of protein transcription factors to cis-regulatory transcription factor binding sites in gene promoter regions. Despite the identification of hundreds of binding site sequence motifs, the question as to whether motif orientation matters with regard to the gene expression regulation of the respective downstream genes appears surprisingly underinvestigated.ResultsWe pursued a statistical approach by probing 293 reported non-palindromic transcription factor binding site and ten core promoter motifs in Arabidopsis thaliana for evidence of any relevance of motif orientation based on mapping statistics and effects on the co-regulation of gene expression of the respective downstream genes. Although positional intervals closer to the transcription start site (TSS) were found with increased frequencies of motifs exhibiting orientation preference, a corresponding effect with regard to gene expression regulation as evidenced by increased co-expression of genes harboring the favored orientation in their upstream sequence could not be established. Furthermore, we identified an intrinsic orientational asymmetry of sequence regions close to the TSS as the likely source of the identified motif orientation preferences. By contrast, motif presence irrespective of orientation was found associated with pronounced effects on gene expression co-regulation validating the pursued approach. Inspecting motif pairs revealed statistically preferred orientational arrangements, but no consistent effect with regard to arrangement-dependent gene expression regulation was evident.ConclusionsOur results suggest that for the motifs considered here, either no specific orientation rendering them functional across all their instances exists with orientational requirements instead depending on gene-locus specific additional factors, or that the binding orientation of transcription factors may generally not be relevant, but rather the event of binding itself.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2549-x) contains supplementary material, which is available to authorized users.

Neural Progenitors Adopt Specific Identities by Directly Repressing All Alternative Progenitor Transcriptional Programs

SummaryIn the vertebrate neural tube, a morphogen-induced transcriptional network produces multiple molecularly distinct progenitor domains, each generating different neuronal subtypes. Using an in vitro differentiation system, we defined gene expression signatures of distinct progenitor populations and identified direct gene-regulatory inputs corresponding to locations of specific transcription factor binding. Combined with targeted perturbations of the network, this revealed a mechanism in which a progenitor identity is installed by active repression of the entire transcriptional programs of other neural progenitor fates. In the ventral neural tube, sonic hedgehog (Shh) signaling, together with broadly expressed transcriptional activators, concurrently activates the gene expression programs of several domains. The specific outcome is selected by repressive input provided by Shh-induced transcription factors that act as the key nodes in the network, enabling progenitors to adopt a single definitive identity from several initially permitted options. Together, the data suggest design principles relevant to many developing tissues.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. We also demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.