TFCat: the curated catalog of mouse and human transcription factors

Neuromolecular responses to social challenge: common mechanisms across mouse, stickleback fish, and honey bee

Certain complex phenotypes appear repeatedly across diverse species due to processes of evolutionary conservation and convergence. In some contexts like developmental body patterning, there is increased appreciation that common molecular mechanisms underlie common phenotypes; these molecular mechanisms include highly conserved genes and networks that may be modified by lineage-specific mutations. However, the existence of deeply conserved mechanisms for social behaviors has not yet been demonstrated. We used a comparative genomics approach to determine whether shared neuromolecular mechanisms could underlie behavioral response to territory intrusion across species spanning a broad phylogenetic range: house mouse (Mus musculus), stickleback fish (Gasterosteus aculeatus), and honey bee (Apis mellifera). Territory intrusion modulated similar brain functional processes in each species, including those associated with hormone-mediated signal transduction and neurodevelopment. Changes in chromosome organization and energy metabolism appear to be core, conserved processes involved in the response to territory intrusion. We also found that several homologous transcription factors that are typically associated with neural development were modulated across all three species, suggesting that shared neuronal effects may involve transcriptional cascades of evolutionarily conserved genes. Furthermore, immunohistochemical analyses of a subset of these transcription factors in mouse again implicated modulation of energy metabolism in the behavioral response. These results provide support for conserved genetic "toolkits" that are used in independent evolutions of the response to social challenge in diverse taxa.

Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes

Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function.

TFClass: a classification of human transcription factors and their rodent orthologs

TFClass aims at classifying eukaryotic transcription factors (TFs) according to their DNA-binding domains (DBDs). For this, a classification schema comprising four generic levels (superclass, class, family and subfamily) was defined that could accommodate all known DNA-binding human TFs. They were assigned to their (sub-)families as instances at two different levels, the corresponding TF genes and individual gene products (protein isoforms). In the present version, all mouse and rat orthologs have been linked to the human TFs, and the mouse orthologs have been arranged in an independent ontology. Many TFs were assigned with typical DNA-binding patterns and positional weight matrices derived from high-throughput in-vitro binding studies. Predicted TF binding sites from human gene upstream sequences are now also attached to each human TF whenever a PWM was available for this factor or one of his paralogs. TFClass is freely available at http://tfclass.bioinf.med.uni-goettingen.de/ through a web interface and for download in OBO format.

Myocardial ischemic preconditioning upregulated protein 1(Mipu1):zinc finger protein 667 - a multifunctional KRAB/C2H2 zinc finger protein

Myocardial ischemic preconditioning upregulated protein 1 (Mipu1) is a newly discovered upregulated gene produced in rats during the myocardial ischemic preconditioning process. Mipu1 cDNA contains a 1824-base pair open reading frame and encodes a 608 amino acid protein with an N-terminal Krüppel-associated box (KRAB) domain and classical zinc finger C2H2 motifs in the C-terminus. Mipu1 protein is located in the cell nucleus. Recent studies found that Mipu1 has a protective effect on the ischemia-reperfusion injury of heart, brain, and other organs. As a nuclear factor, Mipu1 may perform its protective function through directly transcribing and repressing the expression of proapoptotic genes to repress cell apoptosis. In addition, Mipu1 also plays an important role in regulating the gene expression of downstream inflammatory mediators by inhibiting the activation of activator protein-1 and serum response element.

CbGRiTS: cerebellar gene regulation in time and space

The mammalian CNS is one of the most complex biological systems to understand at the molecular level. The temporal information from time series transcriptome analysis can serve as a potent source of associative information between developmental processes and regulatory genes. Here, we introduce a new transcriptome database called, Cerebellar Gene Regulation in Time and Space (CbGRiTS). This dataset is populated with transcriptome data across embryonic and postnatal development from two standard mouse strains, C57BL/6J and DBA/2J, several recombinant inbred lines and cerebellar mutant strains. Users can evaluate expression profiles across cerebellar development in a deep time series with graphical interfaces for data exploration and link-out to anatomical expression databases. We present three analytical approaches that take advantage of specific aspects of the time series for transcriptome analysis. We demonstrate the use of CbGRiTS dataset as a community resource to explore patterns of gene expression and develop hypotheses concerning gene regulatory networks in brain development.

AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors

Transcription factors (TFs) are key regulators for gene expression. Here we updated the animal TF database AnimalTFDB to version 2.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/). Using the improved prediction pipeline, we identified 72 336 TF genes, 21 053 transcription co-factor genes and 6502 chromatin remodeling factor genes from 65 species covering main animal lineages. Besides the abundant annotations (basic information, gene model, protein functional domain, gene ontology, pathway, protein interaction, ortholog and paralog, etc.) in the previous version, we made several new features and functions in the updated version. These new features are: (i) gene expression from RNA-Seq for nine model species, (ii) gene phenotype information, (iii) multiple sequence alignment of TF DNA-binding domains, and the weblogo and phylogenetic tree based on the alignment, (iv) a TF prediction server to identify new TFs from input sequences and (v) a BLAST server to search against TFs in AnimalTFDB. A new nice web interface was designed for AnimalTFDB 2.0 allowing users to browse and search all data in the database. We aim to maintain the AnimalTFDB as a solid resource for TF identification and studies of transcription regulation and comparative genomics.

The transcriptome of utricle hair cell regeneration in the avian inner ear

Sensory hair cell loss is the major cause of hearing and balance disorders. Mammals are incapable of sustained hair cell regeneration, but lower vertebrates can regenerate these mechano-electrical transducers. We present the first comprehensive transcriptome (by mRNA-Seq) of hair cell regeneration in the chick utricle. We provide pathway and pattern annotations and correlate these with the phenotypic events that occur during regeneration. These patterns are surprisingly synchronous and highly punctuated. We show how these patterns are a new resource for identifying components of the hair cell transcriptome and identify 494 new putative hair-cell-specific genes and validate three of these (of three tested) by immunohistochemical staining. We describe many surprising new components and dynamic expression patterns, particularly within NOTCH signaling. For example, we show that HES7 is specifically expressed during utricle hair cell regeneration and closely parallels the expression of HES5. Likewise, the expression of ATOH1 is closely correlated with HEYL and the HLH inhibitory transcription factors ID1, ID2, and ID4. We investigate the correlation between fibroblast growth factor signaling and supporting cell proliferation and show that FGF20 inhibits supporting cell proliferation. We also present an analysis of 212 differentially expressed transcription factor genes in the regenerative time course that fall into nine distinct gene expression patterns, many of which correlate with phenotypic events during regeneration and represent attractive candidates for future analysis and manipulation of the regenerative program in sensory epithelia and other vertebrate neuroepithelia.

Mapping yeast transcriptional networks

The term “transcriptional network” refers to the mechanism(s) that underlies coordinated expression of genes, typically involving transcription factors (TFs) binding to the promoters of multiple genes, and individual genes controlled by multiple TFs. A multitude of studies in the last two decades have aimed to map and characterize transcriptional networks in the yeast Saccharomyces cerevisiae. We review the methodologies and accomplishments of these studies, as well as challenges we now face. For most yeast TFs, data have been collected on their sequence preferences, in vivo promoter occupancy, and gene expression profiles in deletion mutants. These systematic studies have led to the identification of new regulators of numerous cellular functions and shed light on the overall organization of yeast gene regulation. However, many yeast TFs appear to be inactive under standard laboratory growth conditions, and many of the available data were collected using techniques that have since been improved. Perhaps as a consequence, comprehensive and accurate mapping among TF sequence preferences, promoter binding, and gene expression remains an open challenge. We propose that the time is ripe for renewed systematic efforts toward a complete mapping of yeast transcriptional regulatory mechanisms.

Modeling genomic regulatory networks with big data

High-throughput sequencing, large-scale data generation projects, and web-based cloud computing are changing how computational biology is performed, who performs it, and what biological insights it can deliver. I review here the latest developments in available data, methods, and software, focusing on the modeling and analysis of the gene regulatory interactions in cells. Three key findings are: (i) although sophisticated computational resources are increasingly available to bench biologists, tailored ongoing education is necessary to avoid the erroneous use of these resources. (ii) Current models of the regulation of gene expression are far too simplistic and need updating. (iii) Integrative computational analysis of large-scale datasets is becoming a fundamental component of molecular biology. I discuss current and near-term opportunities and challenges related to these three points.

Transcriptome-based network analysis reveals a spectrum model of human macrophage activation

Macrophage activation is associated with profound transcriptional reprogramming. Although much progress has been made in the understanding of macrophage activation, polarization, and function, the transcriptional programs regulating these processes remain poorly characterized. We stimulated human macrophages with diverse activation signals, acquiring a data set of 299 macrophage transcriptomes. Analysis of this data set revealed a spectrum of macrophage activation states extending the current M1 versus M2-polarization model. Network analyses identified central transcriptional regulators associated with all macrophage activation complemented by regulators related to stimulus-specific programs. Applying these transcriptional programs to human alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected loss of inflammatory signatures in COPD patients. Finally, by integrating murine data from the ImmGen project we propose a refined, activation-independent core signature for human and murine macrophages. This resource serves as a framework for future research into regulation of macrophage activation in health and disease.

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Macrophages react with specific transcriptional programming upon distinct signals

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Activation by TNF, PGE₂, and P3C activates a STAT4-associated transcriptional program

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NFKB1, JUNB, and CREB1 are central transcription factors of macrophage activation

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Inflammatory signatures are lost in alveolar macrophages from COPD patients

Role of DNA sequence based structural features of promoters in transcription initiation and gene expression

Regulatory information for transcription initiation is present in a stretch of genomic DNA, called the promoter region that is located upstream of the transcription start site (TSS) of the gene. The promoter region interacts with different transcription factors and RNA polymerase to initiate transcription and contains short stretches of transcription factor binding sites (TFBSs), as well as structurally unique elements. Recent experimental and computational analyses of promoter sequences show that they often have non-B-DNA structural motifs, as well as some conserved structural properties, such as stability, bendability, nucleosome positioning preference and curvature, across a class of organisms. Here, we briefly describe these structural features, the differences observed in various organisms and their possible role in regulation of gene expression.

Spatial expression of transcription factors in Drosophila embryonic organ development

Background

Site-specific transcription factors (TFs) bind DNA regulatory elements to control expression of target genes, forming the core of gene regulatory networks. Despite decades of research, most studies focus on only a small number of TFs and the roles of many remain unknown.

Results

We present a systematic characterization of spatiotemporal gene expression patterns for all known or predicted Drosophila TFs throughout embryogenesis, the first such comprehensive study for any metazoan animal. We generated RNA expression patterns for all 708 TFs by in situ hybridization, annotated the patterns using an anatomical controlled vocabulary, and analyzed TF expression in the context of organ system development. Nearly all TFs are expressed during embryogenesis and more than half are specifically expressed in the central nervous system. Compared to other genes, TFs are enriched early in the development of most organ systems, and throughout the development of the nervous system. Of the 535 TFs with spatially restricted expression, 79% are dynamically expressed in multiple organ systems while 21% show single-organ specificity. Of those expressed in multiple organ systems, 77 TFs are restricted to a single organ system either early or late in development. Expression patterns for 354 TFs are characterized for the first time in this study.

Conclusions

We produced a reference TF dataset for the investigation of gene regulatory networks in embryogenesis, and gained insight into the expression dynamics of the full complement of TFs controlling the development of each organ system.

High-density lipoprotein mediates anti-inflammatory reprogramming of macrophages via the transcriptional regulator ATF3

High Density Lipoprotein (HDL) mediates reverse cholesterol transport and it is known to be protective against atherosclerosis. In addition, HDL has potent anti-inflammatory properties that may be critical for protection against other inflammatory diseases. The molecular mechanisms of how HDL can modulate inflammation, particularly in immune cells such as macrophages, remain poorly understood. Here we identify the transcriptional repressor ATF3, as an HDL-inducible target gene in macrophages that down-regulates the expression of Toll-like receptor (TLR)-induced pro-inflammatory cytokines. The protective effects of HDL against TLR-induced inflammation were fully dependent on ATF3 in vitro and in vivo. Our findings may explain the broad anti-inflammatory and metabolic actions of HDL and provide the basis for predicting the success of novel HDL-based therapies.

Target analysis by integration of transcriptome and ChIP-seq data with BETA

The combination of ChIP-seq and transcriptome analysis is a compelling approach to unravel the regulation of gene expression. Several recently published methods combine transcription factor (TF) binding and gene expression for target prediction, but few of them provide an efficient software package for the community. Binding and expression target analysis (BETA) is a software package that integrates ChIP-seq of TFs or chromatin regulators with differential gene expression data to infer direct target genes. BETA has three functions: (i) to predict whether the factor has activating or repressive function; (ii) to infer the factor's target genes; and (iii) to identify the motif of the factor and its collaborators, which might modulate the factor's activating or repressive function. Here we describe the implementation and features of BETA to demonstrate its application to several data sets. BETA requires ~1 GB of RAM, and the procedure takes 20 min to complete. BETA is available open source at http://cistrome.org/BETA/.

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort

Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information.

Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the controlled vocabularies defined by the Gene Ontology Consortium to annotate specific DNA binding transcription factors (DbTFs) based on experimental evidence reported in literature. Our standardized protocol and workflow for annotating specific DNA binding RNA polymerase II transcription factors is designed to document high-quality and decisive evidence from valid experimental methods. Within a collaborative biocuration effort involving the user community, we are now in the process of exhaustively annotating the full repertoire of human, mouse and rat proteins that qualify as DbTFs in as much as they are experimentally documented in the biomedical literature today. The completion of this task will significantly enrich Gene Ontology-based information resources for the research community.

Database URL:www.tfcheckpoint.org

TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors

SUMMARY

Gene regulatory network assembly and analysis requires high-quality knowledge sources that cover functional aspects of the various components of the gene regulatory machinery. A multiplicity of resources exists with information about mammalian transcription factors (TFs); yet, only few of these provide sufficiently accurate classifications of the functional roles of individual TFs, or standardized evidence that would justify the information on which these functional classifications are based. We compiled the list of all putative TFs from nine different resources, ignored factors such as general TFs, mediator complexes and chromatin modifiers, and for the remaining factors checked the available literature for references that support their function as a true sequence-specific DNA-binding RNA polymerase II TF (DbTF). The results are available in the TFcheckpoint database, an exhaustive collection of TFs annotated according to experimental and other evidence on their function as true DbTFs. TFcheckpoint.org provides a high-quality and comprehensive knowledge source for genome-scale regulatory network studies.

AVAILABILITY

The TFcheckpoint database is freely available at www.tfcheckpoint.org

Unique transcriptome signature of mouse microglia

Microglial cells can be derived directly from the dissociated brain tissue by sorting procedures, from postnatal glial cultures by mechanic isolation or from pluripotent stem cells by differentiation. The detailed molecular phenotype of microglia from different sources is still unclear. Here, we performed a whole transcriptome analysis of flow cytometry-sorted microglia, primary postnatal cultured microglia, embryonic stem cell derived microglia (ESdM), and other cell types. Microglia and ESdM, both cultured in serum-free medium, were closely related to sorted microglia and showed a unique transcriptome profile, clearly distinct to other myeloid cell types, T cells, astrocytes, and neurons. ESdM and primary cultured microglia showed strong overlap in their transcriptome. Only 143 genes were differentially expressed between both cell types, mainly derived from immune-related genes with a higher activation status of proinflammatory and immune defense genes in primary microglia compared to ESdM. Flow cytometry analysis of cell surface markers CD54, CD74, and CD274 selected from the microarray confirmed the close phenotypic relation between ESdM and primary cultured microglia. Thus, assessment of genome-wide transcriptional regulation demonstrates that microglial cells are unique and clearly distinct from other macrophage cell types.

DNA motif elucidation using belief propagation

Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ∼10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors’ websites: e.g. http://www.cs.toronto.edu/∼wkc/kmerHMM.

Role of transcription factors in the pathogenesis of asthma and COPD

Inflammation is a central feature of asthma and chronic obstructive pulmonary disease (COPD). Despite recent advances in the knowledge of the pathogenesis of asthma and COPD, much more research on the molecular mechanisms of asthma and COPD are needed to aid the logical development of new therapies for these common and important diseases, particularly in COPD where no effective treatments currently exist. In the future the role of the activation/repression of different transcription factors and the genetic regulation of their expression in asthma and COPD may be an increasingly important aspect of research, as this may be one of the critical mechanisms regulating the expression of different clinical phenotypes and their responsiveness to therapy, particularly to anti-inflammatory drugs.

CRITERIA FOR AN UPDATED CLASSIFICATION OF HUMAN TRANSCRIPTION FACTOR DNA-BINDING DOMAINS

By binding to cis-regulatory elements in a sequence-specific manner, transcription factors regulate the activity of nearby genes. Here, we discuss the criteria for a comprehensive classification of human TFs based on their DNA-binding domains. In particular, classification of basic leucine zipper (bZIP) and zinc finger factors is exemplarily discussed. The resulting classification can be used as a template for TFs of other biological species.

Multi-scale modeling of gene regulatory networks via integration of temporal and topological biological data

Regulome is the dynamic network representation of the regulatory interplay among genes, proteins and other cellular components that control cellular processes. Reconstruction of gene regulatory networks (GRN) delineates one of the main objectives of Systems Biology towards understanding the organization of regulome. Significant progress has been reported the last years regarding GRN reconstruction methods, but the majority of them either consider information originating solely from gene expression data or/and are applied on a small fraction of the experimental dataset. In this paper, we will describe an integrative method, utilizing both temporal information arriving from time-series gene expression profiles, as well as topological properties of protein networks. The proposed methodology detects relations among either groups of genes or specific genes depending on the level of abstraction or resolution requested. Application on real data proved the ability of the method to extract relations in accordance with current biological knowledge as well as discriminate between different experimental conditions.

Cys2His2 zinc finger protein family: classification, functions, and major members

Cys2His2 (C2H2)-type zinc fingers are widespread DNA binding motifs in eukaryotic transcription factors. Zinc fingers are short protein motifs composed of two or three β-layers and one α-helix. Two cysteine and two histidine residues located in certain positions bind zinc to stabilize the structure. Four other amino acid residues localized in specific positions in the N-terminal region of the α-helix participate in DNA binding by interacting with hydrogen donors and acceptors exposed in the DNA major groove. The number of zinc fingers in a single protein can vary over a wide range, thus enabling variability of target DNA sequences. Besides DNA binding, zinc fingers can also provide protein-protein and RNA-protein interactions. For the most part, proteins containing the C2H2-type zinc fingers are trans regulators of gene expression that play an important role in cellular processes such as development, differentiation, and suppression of malignant cell transformation (oncosuppression).

Molecular pathways involved in the improvement of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis are components of the metabolic syndrome. Serum leptin levels are elevated in obesity, but the role of leptin in the pathophysiology of the liver involvement is still unclear. To identify the effects and mechanisms by which leptin influences the pathogenesis of NAFLD, we performed epididymal white adipose tissue (eWAT) transplantation from congenic wild-type mice into the subcutaneous dorsal area of Lep(ob/ob) recipient mice and compared the results with those of the Lep(ob/ob) sham-operated mice. The mice were followed for 102-216 days. During killing, the transplanted mice had significantly lost body weight and exhibited significantly higher leptin levels, improved glucose tolerance, and lower liver injury scores than the sham-operated mice. Liver microarray analysis showed that novel pathways related to GA-binding protein (GABP) transcription factor targets, pheromone binding, and olfactory signaling were differentially expressed in the transplanted mice. Our data also replicate pathways known to be involved in NAFLD, such as those involved in the regulation of microRNAs, lipid, glucose, and glutathione metabolism, peroxisome proliferator-activated receptor signaling, cellular regulation, carboxylic acid processes, iron, heme, and tetrapyrrole binding, immunity and inflammation, insulin signaling, cytochrome P450 function, and cancer.

CONCLUSION

wild-type eWAT transplantation into Lep(ob/ob) mice led to improvements in metabolism, body weight, and liver injury, possibly attributed to the production of leptin by the transplanted eWAT. These improvements were accompanied by the differential expression of novel pathways. The causal relationship between GABP downregulation and NAFLD improvement remains to be determined.

TFClass: an expandable hierarchical classification of human transcription factors

TFClass (http://tfclass.bioinf.med.uni-goettingen.de/) provides a comprehensive classification of human transcription factors based on their DNA-binding domains. Transcription factors constitute a large functional family of proteins directly regulating the activity of genes. Most of them are sequence-specific DNA-binding proteins, thus reading out the information encoded in cis-regulatory DNA elements of promoters, enhancers and other regulatory regions of a genome. TFClass is a database that classifies human transcription factors by a six-level classification schema, four of which are abstractions according to different criteria, while the fifth level represents TF genes and the sixth individual gene products. Altogether, nine superclasses have been identified, comprising 40 classes and 111 families. Counted by genes, 1558 human TFs have been classified so far or >2900 different TFs when including their isoforms generated by alternative splicing or protein processing events. With this classification, we hope to provide a basis for deciphering protein-DNA recognition codes; moreover, it can be used for constructing expanded transcriptional networks by inferring additional TF-target gene relations.

The transcription factor encyclopedia

Here we present the Transcription Factor Encyclopedia (TFe), a new web-based compendium of mini review articles on transcription factors (TFs) that is founded on the principles of open access and collaboration. Our consortium of over 100 researchers has collectively contributed over 130 mini review articles on pertinent human, mouse and rat TFs. Notable features of the TFe website include a high-quality PDF generator and web API for programmatic data retrieval. TFe aims to rapidly educate scientists about the TFs they encounter through the delivery of succinct summaries written and vetted by experts in the field. TFe is available at http://www.cisreg.ca/tfe.

oPOSSUM-3: advanced analysis of regulatory motif over-representation across genes or ChIP-Seq datasets

oPOSSUM-3 is a web-accessible software system for identification of over-represented transcription factor binding sites (TFBS) and TFBS families in either DNA sequences of co-expressed genes or sequences generated from high-throughput methods, such as ChIP-Seq. Validation of the system with known sets of co-regulated genes and published ChIP-Seq data demonstrates the capacity for oPOSSUM-3 to identify mediating transcription factors (TF) for co-regulated genes or co-recovered sequences. oPOSSUM-3 is available at http://opossum.cisreg.ca.

Finding biomarkers in non-model species: literature mining of transcription factors involved in bovine embryo development

Background

Since processes in well-known model organisms have specific features different from those in Bos taurus, the organism under study, a good way to describe gene regulation in ruminant embryos would be a species-specific consideration of closely related species to cattle, sheep and pig. However, as highlighted by a recent report, gene dictionaries in pig are smaller than in cattle, bringing a risk to reduce the gene resources to be mined (and so for sheep dictionaries). Bioinformatics approaches that allow an integration of available information on gene function in model organisms, taking into account their specificity, are thus needed. Besides these closely related and biologically relevant species, there is indeed much more knowledge of (i) trophoblast proliferation and differentiation or (ii) embryogenesis in human and mouse species, which provides opportunities for reconstructing proliferation and/or differentiation processes in other mammalian embryos, including ruminants. The necessary knowledge can be obtained partly from (i) stem cell or cancer research to supply useful information on molecular agents or molecular interactions at work in cell proliferation and (ii) mouse embryogenesis to supply useful information on embryo differentiation. However, the total number of publications for all these topics and species is great and their manual processing would be tedious and time consuming. This is why we used text mining for automated text analysis and automated knowledge extraction. To evaluate the quality of this “mining”, we took advantage of studies that reported gene expression profiles during the elongation of bovine embryos and defined a list of transcription factors (or TF, n = 64) that we used as biological “gold standard”. When successful, the “mining” approach would identify them all, as well as novel ones.

Methods

To gain knowledge on molecular-genetic regulations in a non model organism, we offer an approach based on literature-mining and score arrangement of data from model organisms. This approach was applied to identify novel transcription factors during bovine blastocyst elongation, a process that is not observed in rodents and primates. As a result, searching through human and mouse corpuses, we identified numerous bovine homologs, among which 11 to 14% of transcription factors including the gold standard TF as well as novel TF potentially important to gene regulation in ruminant embryo development. The scripts of the workflow are written in Perl and available on demand. They require data input coming from all various databases for any kind of biological issue once the data has been prepared according to keywords for the studied topic and species; we can provide data sample to illustrate the use and functionality of the workflow.

Results

To do so, we created a workflow that allowed the pipeline processing of literature data and biological data, extracted from Web of Science (WoS) or PubMed but also from Gene Expression Omnibus (GEO), Gene Ontology (GO), Uniprot, HomoloGene, TcoF-DB and TFe (TF encyclopedia). First, the human and mouse homologs of the bovine proteins were selected, filtered by text corpora and arranged by score functions. The score functions were based on the gene name frequencies in corpora. Then, transcription factors were identified using TcoF-DB and double-checked using TFe to characterise TF groups and families. Thus, among a search space of 18,670 bovine homologs, 489 were identified as transcription factors. Among them, 243 were absent from the high-throughput data available at the time of the study. They thus stand so far for putative TF acting during bovine embryo elongation, but might be retrieved from a recent RNA sequencing dataset (Mamo et al. , 2012). Beyond the 246 TF that appeared expressed in bovine elongating tissues, we restricted our interpretation to those occurring within a list of 50 top-ranked genes. Among the transcription factors identified therein, half belonged to the gold standard (ASCL2, c-FOS, ETS2, GATA3, HAND1) and half did not (ESR1, HES1, ID2, NANOG, PHB2, TP53, STAT3).

Conclusions

A workflow providing search for transcription factors acting in bovine elongation was developed. The model assumed that proteins sharing the same protein domains in closely related species had the same protein functionalities, even if they were differently regulated among species or involved in somewhat different pathways. Under this assumption, we merged the information on different mammalian species from different databases (literature and biology) and proposed 489 TF as potential participants of embryo proliferation and differentiation, with (i) a recall of 95% with regard to a biological gold standard defined in 2011 and (ii) an extension of more than 3 times the gold standard of TF detected so far in elongating tissues. The working capacity of the workflow was supported by the manual expertise of the biologists on the results. The workflow can serve as a new kind of bioinformatics tool to work on fused data sources and can thus be useful in studies of a wide range of biological processes.

RBFOX1 regulates both splicing and transcriptional networks in human neuronal development

RNA splicing plays a critical role in the programming of neuronal differentiation and, consequently, normal human neurodevelopment, and its disruption may underlie neurodevelopmental and neuropsychiatric disorders. The RNA-binding protein, fox-1 homolog (RBFOX1; also termed A2BP1 or FOX1), is a neuron-specific splicing factor predicted to regulate neuronal splicing networks clinically implicated in neurodevelopmental disease, including autism spectrum disorder (ASD), but only a few targets have been experimentally identified. We used RNA sequencing to identify the RBFOX1 splicing network at a genome-wide level in primary human neural stem cells during differentiation. We observe that RBFOX1 regulates a wide range of alternative splicing events implicated in neuronal development and maturation, including transcription factors, other splicing factors and synaptic proteins. Downstream alterations in gene expression define an additional transcriptional network regulated by RBFOX1 involved in neurodevelopmental pathways remarkably parallel to those affected by splicing. Several of these differentially expressed genes are further implicated in ASD and related neurodevelopmental diseases. Weighted gene co-expression network analysis demonstrates a high degree of connectivity among these disease-related genes, highlighting RBFOX1 as a key factor coordinating the regulation of both neurodevelopmentally important alternative splicing events and clinically relevant neuronal transcriptional programs in the development of human neurons.

A flexible integrative approach based on random forest improves prediction of transcription factor binding sites

Transcription factor binding sites (TFBSs) are DNA sequences of 6–15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.

Novel insights into the downstream pathways and targets controlled by transcription factors CREM in the testis

The essential role of the Crem gene in normal sperm development is widely accepted and is confirmed by azoospermia in male mice lacking the Crem gene. The exact number of genes affected by Crem absence is not known, however a large difference has been observed recently between the estimated number of differentially expressed genes found in Crem knock-out (KO) mice compared to the number of gene loci bound by CREM. We therefore re-examined global gene expression in male mice lacking the Crem gene using whole genome transcriptome analysis with Affymetrix microarrays and compared the lists of differentially expressed genes from Crem-/- mice to a dataset of genes where binding of CREM was determined by Chip-seq. We determined the global effect of CREM on spermatogenesis as well as distinguished between primary and secondary effects of the CREM absence. We demonstrated that the absence of Crem deregulates over 4700 genes in KO testis. Among them are 101 genes associated with spermatogenesis 41 of which are bound by CREM and are deregulated in Crem KO testis. Absence of several of these genes in mouse models has proven their importance for normal spermatogenesis and male fertility. Our study showed that the absence of Crem plays a more important role on different aspects of spermatogenesis as estimated previously, with its impact ranging from apoptosis induction to deregulation of major circadian clock genes, steroidogenesis and the cell-cell junction dynamics. Several new genes important for normal spermatogenesis and fertility are down-regulated in KO testis and are therefore possible novel targets of CREM.

Genome-wide analysis of STAT3 binding in vivo predicts effectors of the anti-inflammatory response in macrophages

Inflammation is a powerful response of the immune system against invading pathogens, and must be cancelled when unneeded or otherwise death inevitably follows. In macrophages, the anti-inflammatory response (AIR) is driven by STAT3 upon IL-10 signaling. The role of STAT3 is to stimulate the expression of specific genes that in-turn suppress the transcription of proinflammatory genes. Here we describe a systematic approach to identify the elusive STAT3-controlled effectors of the AIR. In vivo STAT3-binding sites were identified by ChIP-seq, coupled to expression analysis by RNA-seq, both in resting and IL-10-treated peritoneal macrophages. We report the genomic targets of STAT3 and show that STAT3's transcriptional program during the AIR is highly specific to IL-10-stimulated macrophages, that STAT3 is a positive transcriptional regulator, and we predict severalputative AIR factors that merit further investigation. This is the first in-depth study of the AIR by next-generation sequencing and provides an unprecedented degree of detail into this fundamental physiologic response.

How do you find transcription factors? Computational approaches to compile and annotate repertoires of regulators for any genome

Transcription factors (TFs) play an important role in regulating gene expression. The availability of complete genome sequences and associated functional genomic data offer excellent opportunities to understand the transcriptional regulatory system of an entire organism. To do so, however, it is essential to compile a reliable dataset of regulatory components. Here, we review computational methods and publicly accessible resources that help identify TF-coding genes in prokaryotic and eukaryotic genomes. Since the regulatory functions of most TFs remain unknown, we also discuss approaches for combining diverse genomic datasets that will help elucidate their chromosomal organisation, expression, and evolutionary conservation. These analysis methods provide a solid foundation for further investigations of the transcriptional regulatory system.

Analysis of early C2C12 myogenesis identifies stably and differentially expressed transcriptional regulators whose knock-down inhibits myoblast differentiation

Myogenesis is a tightly controlled process involving the transcriptional activation and repression of thousands of genes. Although many components of the transcriptional network regulating the later phases of myogenesis have been identified, relatively few studies have described the transcriptional landscape during the first 24 h, when myoblasts commit to differentiate. Through dense temporal profiling of differentiating C2C12 myoblasts, we identify 193 transcriptional regulators (TRs) whose expression is significantly altered within the first 24 h of myogenesis. A high-content shRNA screen of 77 TRs involving 427 stable lines identified 42 genes whose knockdown significantly inhibits differentiation of C2C12 myoblasts. Of the TRs that were differentially expressed within the first 24 h, over half inhibited differentiation when knocked down, including known regulators of myogenesis (Myod1, Myog, and Myf5), as well as 19 TRs not previously associated with this process. Surprisingly, a similar proportion (55%) of shRNAs targeting TRs whose expression did not change also inhibited C2C12 myogenesis. We further show that a subset of these TRs inhibits myogenesis by downregulating expression of known regulatory and structural proteins. Our findings clearly illustrate that several TRs critical for C2C12 myogenesis are not differentially regulated, suggesting that approaches that focus functional studies on differentially-expressed transcripts will fail to provide a comprehensive view of this complex process.

AnimalTFDB: a comprehensive animal transcription factor database

Transcription factors (TFs) are proteins that bind to specific DNA sequences, thereby playing crucial roles in gene-expression regulation through controlling the transcription of genetic information from DNA to RNA. Transcription cofactors and chromatin remodeling factors are also essential in the gene transcriptional regulation. Identifying and annotating all the TFs are primary and crucial steps for illustrating their functions and understanding the transcriptional regulation. In this study, based on manual literature reviews, we collected and curated 72 TF families for animals, which is currently the most complete list of TF families in animals. Then, we systematically characterized all the TFs in 50 animal species and constructed a comprehensive animal TF database, AnimalTFDB. To better serve the community, we provided detailed annotations for each TF, including basic information, gene structure, functional domain, 3D structure hit, Gene Ontology, pathway, protein–protein interaction, paralogs, orthologs, potential TF-binding sites and targets. In addition, we collected and annotated transcription cofactors and chromatin remodeling factors. AnimalTFDB has a user-friendly web interface with multiple browse and search functions, as well as data downloading. It is freely available at http://www.bioguo.org/AnimalTFDB/.

Yeast one-hybrid assays for gene-centered human gene regulatory network mapping

Gateway-compatible yeast one-hybrid (Y1H) assays provide a convenient gene-centered (DNA to protein) approach to identify transcription factors that can bind a DNA sequence of interest. We present Y1H resources, including clones for 988 of 1,434 (69%) predicted human transcription factors, that can be used to detect both known and new interactions between human DNA regions and transcription factors.

Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection

BACKGROUND

The immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.

RESULTS

We applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.

CONCLUSIONS

Our results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

Transcriptional networks in epithelial-mesenchymal transition

Backround

Epithelial-mesenchymal transition (EMT) changes polarized epithelial cells into migratory phenotypes associated with loss of cell-cell adhesion molecules and cytoskeletal rearrangements. This form of plasticity is seen in mesodermal development, fibroblast formation, and cancer metastasis.

Methods and Findings

Here we identify prominent transcriptional networks active during three time points of this transitional process, as epithelial cells become fibroblasts. DNA microarray in cultured epithelia undergoing EMT, validated in vivo, were used to detect various patterns of gene expression. In particular, the promoter sequences of differentially expressed genes and their transcription factors were analyzed to identify potential binding sites and partners. The four most frequent cis-regulatory elements (CREs) in up-regulated genes were SRY, FTS-1, Evi-1, and GC-Box, and RNA inhibition of the four transcription factors, Atf2, Klf10, Sox11, and SP1, most frequently binding these CREs, establish their importance in the initiation and propagation of EMT. Oligonucleotides that block the most frequent CREs restrain EMT at early and intermediate stages through apoptosis of the cells.

Conclusions

Our results identify new transcriptional interactions with high frequency CREs that modulate the stability of cellular plasticity, and may serve as targets for modulating these transitional states in fibroblasts.

Conserved and differential gene interactions in dynamical biological systems

MOTIVATION

While biological systems operated from a common genome can be conserved in various ways, they can also manifest highly diverse dynamics and functions. This is because the same set of genes can interact differentially across specific molecular contexts. For example, differential gene interactions give rise to various stages of morphogenesis during cerebellar development. However, after over a decade of efforts toward reverse engineering biological networks from high-throughput omic data, gene networks of most organisms remain sketchy. This hindrance has motivated us to develop comparative modeling to highlight conserved and differential gene interactions across experimental conditions, without reconstructing complete gene networks first.

RESULTS

We established a comparative dynamical system modeling (CDSM) approach to identify conserved and differential interactions across molecular contexts. In CDSM, interactions are represented by ordinary differential equations and compared across conditions through statistical heterogeneity and homogeneity tests. CDSM demonstrated a consistent superiority over differential correlation and reconstruct-then-compare in simulation studies. We exploited CDSM to elucidate gene interactions important for cellular processes poorly understood during mouse cerebellar development. We generated hypotheses on 66 differential genetic interactions involved in expansion of the external granule layer. These interactions are implicated in cell cycle, differentiation, apoptosis and morphogenesis. Additional 1639 differential interactions among gene clusters were also identified when we compared gene interactions during the presence of Rhombic lip versus the presence of distinct internal granule layer. Moreover, compared with differential correlation and reconstruct-then-compare, CDSM makes fewer assumptions on data and thus is applicable to a wider range of biological assays.

AVAILABILITY

Source code in C++ and R is available for non-commercial organizations upon request from the corresponding author. The cerebellum gene expression dataset used in this article is available upon request from the Goldowitz lab (dang@cmmt.ubc.ca, http://grits.dglab.org/).

CONTACT

joemsong@cs.nmsu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Endogenous tagging of the murine transcription factor Sox5 with hemaglutinin for functional studies

Gene expression is usually studied at the transcript level rather than at the protein level due to the lack of a specific and sensitive antibody. A way to overcome this is to fuse to the protein of interest an immunoreactive tag that has well-characterized antibodies. This epitope tagging approach is often used for in vitro experiments but for in vivo studies, the success rate of protein tagging has not been extensively analyzed and our study seeks to cover the void. A small epitope, hemaglutinin derived from the influenza virus was used to tag a transcription factor, Sox5 at the N-terminal via homologous recombination in the mouse. Sox5 is part of the Sry-related high-mobility-group box gene family and plays multiple roles in essential biological processes. Understanding of its molecular function in relation to its biological roles remains incomplete. In our study, we show that the longer isoform of Sox5 can be tagged endogenously with hemaglutinin without affecting its biological function in vivo. The tagged protein is easily and specifically detected with an anti-hemaglutinin antibody using immunohistochemistry with its expression matching the endogenous expression of Sox5. Immunoprecipitation of Sox5 was also carried out successfully using an anti-hemaglutinin antibody. The transgenic line generated from this study is predicted to be useful for future experiments such as co-immunoprecipitation and chromatin immunoprecipitation, allowing the further understanding of Sox5. Lastly, this approach can be easily employed for the investigation of other transcription factors and proteins in vivo to overcome technical limitations such as antibody cross-reactivity and to perform isoform-specific studies.

Towards resolving the transcription factor network controlling myelin gene expression

In the central nervous system (CNS), myelin is produced from spirally-wrapped oligodendrocyte plasma membrane and, as exemplified by the debilitating effects of inherited or acquired myelin abnormalities in diseases such as multiple sclerosis, it plays a critical role in nervous system function. Myelin sheath production coincides with rapid up-regulation of numerous genes. The complexity of their subsequent expression patterns, along with recently recognized heterogeneity within the oligodendrocyte lineage, suggest that the regulatory networks controlling such genes drive multiple context-specific transcriptional programs. Conferring this nuanced level of control likely involves a large repertoire of interacting transcription factors (TFs). Here, we combined novel strategies of computational sequence analyses with in vivo functional analysis to establish a TF network model of coordinate myelin-associated gene transcription. Notably, the network model captures regulatory DNA elements and TFs known to regulate oligodendrocyte myelin gene transcription and/or oligodendrocyte development, thereby validating our approach. Further, it links to numerous TFs with previously unsuspected roles in CNS myelination and suggests collaborative relationships amongst both known and novel TFs, thus providing deeper insight into the myelin gene transcriptional network.

Sequence specificity is obtained from the majority of modular C2H2 zinc-finger arrays

C2H2 zinc fingers (C2H2-ZFs) are the most prevalent type of vertebrate DNA-binding domain, and typically appear in tandem arrays (ZFAs), with sequential C2H2-ZFs each contacting three (or more) sequential bases. C2H2-ZFs can be assembled in a modular fashion, providing one explanation for their remarkable evolutionary success. Given a set of modules with defined three-base specificities, modular assembly also presents a way to construct artificial proteins with specific DNA-binding preferences. However, a recent survey of a large number of three-finger ZFAs engineered by modular assembly reported high failure rates (∼70%), casting doubt on the generality of modular assembly. Here, we used protein-binding microarrays to analyze 28 ZFAs that failed in the aforementioned study. Most (17) preferred specific sequences, which in all but one case resembled the intended target sequence. Like natural ZFAs, the engineered ZFAs typically yielded degenerate motifs, binding dozens to hundreds of related individual sequences. Thus, the failure of these proteins in previous assays is not due to lack of sequence-specific DNA-binding activity. Our findings underscore the relevance of individual C2H2-ZF sequence specificities within tandem arrays, and support the general ability of modular assembly to produce ZFAs with sequence-specific DNA-binding activity.

Uncovering the transcriptional circuitry in skeletal muscle regeneration

Skeletal muscle has a remarkable ability to regenerate after repeated and complete destruction of the tissue. The healing phases for an injured muscle undergo an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network is confronted by significant challenges and requires the integration of multiple experimental data types. In this work we present a system approach to describe the transcriptional circuitry during skeletal muscle regeneration using time-course expression data and motif scanning information. Time-lagged correlation analysis was utilized to evaluate the transcription factor (TF) → target associations. Our analysis identified six TFs that potentially play a central role throughout the regeneration process. Four of them have previously been described to be important for muscle regeneration and differentiation. The remaining two TFs are identified as novel regulators that may have a role in the regeneration process. We hope that our work may provide useful clues to help accelerate the recovery process in injured skeletal muscle.

Systems biology of interstitial lung diseases: integration of mRNA and microRNA expression changes

Background

The molecular pathways involved in the interstitial lung diseases (ILDs) are poorly understood. Systems biology approaches, with global expression data sets, were used to identify perturbed gene networks, to gain some understanding of the underlying mechanisms, and to develop specific hypotheses relevant to these chronic lung diseases.

Methods

Lung tissue samples from patients with different types of ILD were obtained from the Lung Tissue Research Consortium and total cell RNA was isolated. Global mRNA and microRNA were profiled by hybridization and amplification-based methods. Differentially expressed genes were compiled and used to identify critical signaling pathways and potential biomarkers. Modules of genes were identified that formed a regulatory network, and studies were performed on cultured cells in vitro for comparison with the in vivo results.

Results

By profiling mRNA and microRNA (miRNA) expression levels, we found subsets of differentially expressed genes that distinguished patients with ILDs from controls and that correlated with different disease stages and subtypes of ILDs. Network analysis, based on pathway databases, revealed several disease-associated gene modules, involving genes from the TGF-β, Wnt, focal adhesion, and smooth muscle actin pathways that are implicated in advancing fibrosis, a critical pathological process in ILDs. A more comprehensive approach was also adapted to construct a putative global gene regulatory network based on the perturbation of key regulatory elements, transcription factors and microRNAs. Our data underscores the importance of TGF-β signaling and the persistence of smooth muscle actin-containing fibroblasts in these diseases. We present evidence that, downstream of TGF-β signaling, microRNAs of the miR-23a cluster and the transcription factor Zeb1 could have roles in mediating an epithelial to mesenchymal transition (EMT) and the resultant persistence of mesenchymal cells in these diseases.

Conclusions

We present a comprehensive overview of the molecular networks perturbed in ILDs, discuss several potential key molecular regulatory circuits, and identify microRNA species that may play central roles in facilitating the progression of ILDs. These findings advance our understanding of these diseases at the molecular level, provide new molecular signatures in defining the specific characteristics of the diseases, suggest new hypotheses, and reveal new potential targets for therapeutic intervention.

A catalogue of eukaryotic transcription factor types, their evolutionary origin, and species distribution

Transcription factors (TFs) play key roles in the regulation of gene expression by binding in a sequence-specific manner to genomic DNA. In eukaryotes, DNA binding is achieved by a wide range of structural forms and motifs. TFs are typically classified by their DNA-binding domain (DBD) type. In this chapter, we catalogue and survey 91 different TF DBD types in metazoa, plants, fungi, and protists. We briefly discuss well-characterized TF families representing the major DBD superclasses. We also examine the species distributions and inferred evolutionary histories of the various families, and the potential roles played by TF family expansion and dimerization.

Introduction to "a handbook of transcription factors"

This chapter briefly summarizes the topics in this volume.

Methods for Analysis of Transcription Factor DNA-Binding Specificity In Vitro

Transcription of genes during development and in response to environmental stimuli is determined by genomic DNA sequence. The DNA sequences regulating transcription are read by sequence-specific transcription factors (TFs) that recognize relatively short sequences, generally between four and twenty base pairs in length. Transcriptional regulation generally requires binding of multiple TFs in close proximity to each other. Mechanistic understanding of transcription in an organism thus requires detailed knowledge of binding affinities of all its TFs to all possible DNA sequences, and the co-operative interactions between the TFs. However, very little is known about such co-operative binding interactions, and even the simple TF-DNA binding information exists only for a very small proportion of all TFs - for example, mammals have approximately 1,300-2,000 TFs [1, 2], yet the largest public databases for TF binding specificity, Jaspar and Uniprobe [3, 4] currently list only approximately 500 moderate to high resolution profiles for human or mouse. This lack of knowledge is in part due to the fact that analysis of TF DNA binding has been laborious and expensive. In this chapter, we review methods that can be used to determine binding specificity of TFs to DNA, mainly focusing on recently developed assays that allow high-resolution analysis of TF binding specificity in relatively high throughput.

TcoF-DB: dragon database for human transcription co-factors and transcription factor interacting proteins

The initiation and regulation of transcription in eukaryotes is complex and involves a large number of transcription factors (TFs), which are known to bind to the regulatory regions of eukaryotic DNA. Apart from TF–DNA binding, protein–protein interaction involving TFs is an essential component of the machinery facilitating transcriptional regulation. Proteins that interact with TFs in the context of transcription regulation but do not bind to the DNA themselves, we consider transcription co-factors (TcoFs). The influence of TcoFs on transcriptional regulation and initiation, although indirect, has been shown to be significant with the functionality of TFs strongly influenced by the presence of TcoFs. While the role of TFs and their interaction with regulatory DNA regions has been well-studied, the association between TFs and TcoFs has so far been given less attention. Here, we present a resource that is comprised of a collection of human TFs and the TcoFs with which they interact. Other proteins that have a proven interaction with a TF, but are not considered TcoFs are also included. Our database contains 157 high-confidence TcoFs and additionally 379 hypothetical TcoFs. These have been identified and classified according to the type of available evidence for their involvement in transcriptional regulation and their presence in the cell nucleus. We have divided TcoFs into four groups, one of which contains high-confidence TcoFs and three others contain TcoFs which are hypothetical to different extents. We have developed the Dragon Database for Human Transcription Co-Factors and Transcription Factor Interacting Proteins (TcoF-DB). A web-based interface for this resource can be freely accessed at http://cbrc.kaust.edu.sa/tcof/ and http://apps.sanbi.ac.za/tcof/.

High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions

Accurately modeling the DNA sequence preferences of transcription factors (TFs), and using these models to predict in vivo genomic binding sites for TFs, are key pieces in deciphering the regulatory code. These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices (PSSMs), which may match large numbers of sites and produce an unreliable list of target genes. Recently, protein binding microarray (PBM) experiments have emerged as a new source of high resolution data on in vitro TF binding specificities. PBM data has been analyzed either by estimating PSSMs or via rank statistics on probe intensities, so that individual sequence patterns are assigned enrichment scores (E-scores). This representation is informative but unwieldy because every TF is assigned a list of thousands of scored sequence patterns. Meanwhile, high-resolution in vivo TF occupancy data from ChIP-seq experiments is also increasingly available. We have developed a flexible discriminative framework for learning TF binding preferences from high resolution in vitro and in vivo data. We first trained support vector regression (SVR) models on PBM data to learn the mapping from probe sequences to binding intensities. We used a novel -mer based string kernel called the di-mismatch kernel to represent probe sequence similarities. The SVR models are more compact than E-scores, more expressive than PSSMs, and can be readily used to scan genomics regions to predict in vivo occupancy. Using a large data set of yeast and mouse TFs, we found that our SVR models can better predict probe intensity than the E-score method or PBM-derived PSSMs. Moreover, by using SVRs to score yeast, mouse, and human genomic regions, we were better able to predict genomic occupancy as measured by ChIP-chip and ChIP-seq experiments. Finally, we found that by training kernel-based models directly on ChIP-seq data, we greatly improved in vivo occupancy prediction, and by comparing a TF's in vitro and in vivo models, we could identify cofactors and disambiguate direct and indirect binding.