Identify Transcription Factor Binding Sites Research Articles

Comparison of different motif discovery algorithms

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

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

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

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) methods have been widely applied to identify TF binding sites and predict motif patterns, with the strengths of offering a scalable, flexible and unified computational approach for highly accurate predictions. As far as we know, 20 DL methods have been developed. However, without a clear and systematic assessment, users will struggle to choose the most appropriate tool for their specific studies. In this manuscript, we evaluated 20 DL methods for cis-regulatory motif prediction using 690 ENCODE ChIP-seq, 126 cancer ChIP-seq and 55 RNA CLIP-seq data. Four metrics were investigated, including the accuracy of motif finding, the performance of DNA/RNA sequence classification, algorithm scalability and tool usability. The assessment results demonstrated the high complementarity of the existing DL methods. It was determined that the most suitable model should primarily depend on the data size and type and the method’s outputs.

Evaluation of deep learning approaches for modeling transcription factor sequence specificity

As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identify TF binding sites and predict their affinities. Recently, deep learning (DL) algorithms have achieved promising results in the prediction of DNA-TF binding, however, various deep learning architectures have not been systematically compared, and the relative merit of each architecture remains unclear. To address this problem, we applied four different deep learning architectures to SELEX-seq and HT-SELEX data, covering three species and 35 families. We evaluated and compared the performance of different deep neural models using 10-fold cross-validation. Our results indicate that the hybrid CNN + DNN model shows the best performances. We expect that our study will be broadly applicable to modeling and predicting TF binding specificity when more high-throughput affinity data are available.

Profiling of Pluripotency Factors in Single Cells and Early Embryos

Cell fate decisions are governed by sequence-specific transcription factors (TFs) that act in small populations of cells within developing embryos. To understand their functions invivo, it is important to identify TF binding sites in these cells. However, current methods cannot profile TFs genome-wide at ornear the single-cell level. Here we adapt the cleavage under targets and release using nuclease (CUT&RUN) method to profile TFs in low cell numbers, including single cells and individual pre-implantation embryos. Single-cell experiments suggest that only a fraction of TF binding sites are occupied in most cells, in a manner broadly consistent with measurements of peak intensity from multi-cell studies. We further show that chromatin binding by the pluripotency TF NANOG is highly dependent on the SWI/SNF chromatin remodeling complex in individual blastocysts but not in cultured cells. Ultra-low input CUT&RUN (uliCUT&RUN) therefore enables interrogation of TF binding from rare cell populations of particular importance in development or disease.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

Background: Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery. Methods: Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach. Results: Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum. Conclusion: Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

Recurrent Neural Network for Predicting Transcription Factor Binding Sites

It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on k-mer features or convolutional neural networks, have been proposed to identify TF binding sites from DNA sequences. Although these methods have good performance, the context information that relates to TF binding sites is still lacking. Research indicates that standard recurrent neural networks (RNN) and its variants have better performance in time-series data compared with other models. In this study, we propose a model, named KEGRU, to identify TF binding sites by combining Bidirectional Gated Recurrent Unit (GRU) network with k-mer embedding. Firstly, DNA sequences are divided into k-mer sequences with a specified length and stride window. And then, we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm. Thirdly, we construct a deep bidirectional GRU model for feature learning and classification. Experimental results have shown that our method has better performance compared with some state-of-the-art methods. Additional experiments about embedding strategy show that k-mer embedding will be helpful to enhance model performance. The robustness of KEGRU is proved by experiments with different k-mer length, stride window and embedding vector dimension.

T197. A DRD2 CO-EXPRESSION GENE SET ENRICHED FOR SCHIZOPHRENIA RISK GENES IS CHARACTERIZED BY A COMMON TRANSCRIPTIONAL REGULATION INVOLVING NURR1 TRANSCRIPTION FACTOR

BackgroundGenome-wide association studies demonstrated that multiple genetic variants are associated with schizophrenia (SCZ). Additional evidence revealed that genes are prone to operate in functional molecular networks that subtend complex clinical phenotypes. This knowledge raises the need to investigate how genes linked to SCZ and their possible co-regulators are inserted into molecular networks with a key impact in disease pathogenesis. Using post-mortem brain mRNA data sets (Pergola et al. 2017), we have previously identified a co-expression network enriched for SCZ risk genes, including DRD2, the gene coding for the D2 dopamine receptor, and predicted cognitive and neuroimaging phenotypes of SCZ, as well as response to antipsychotic treatment. Given the relevance of DRD2 to the pathophysiology of SCZ, in the current study we sought to further our understanding of biological mechanisms underpinning co-expression of the DRD2 network. In detail, we aimed at probing the hypothesis that expression of genes within the DRD2-related co-expression network is modulated by a common transcriptional regulation involving one or more Transcription factors (TFs).MethodsIn order to identify TF binding sites (TFBSs) in the promoter region of the 85 genes belonging to the DRD2 co-expression network, we performed a motif enrichment analysis using Pscan and Genomatix MatInspector tools. Biological validation experiments were performed in primary mouse cortical neurons. By real-time PCR analysis we measured the mRNA transcript levels of a group of genes included in the DRD2 co-expression module in basal conditions and upon viral vector-mediated overexpression (OE) and knockdown (KD) of the predicted TFs. We studied expression of genes linked to either DRD2 in the co-expression gene set, such as Gpld1, Chit1, Btg4 and Osr1, or SCZ risk, such as Gatad2a and Slc28a1. We also analyzed transcript levels of Cnr1, which mediates cannabinoid-induced transmission and is relevant to SCZ. Moreover, we analyzed the transcript levels of D2 long splicing isoform (D2L), which was included in the co-expression network, and D2 short splicing isoform (D2S), to verify whether TF regulation was specific for D2L.ResultsPromoters of the DRD2 co-expression gene set were enriched for two TFBSs, recognized by Nur-Related Factor 1 (NURR1, FDR-adjusted p=0.03) and Estrogen-Related Receptor Alpha (ERR1, FDR-adjusted p=0.02), respectively. Validation experiments in mouse primary cortical neurons established that NURR1, and not ERR1, is a regulator of genes of the DRD2 co-expression module analyzed in this study. In detail, Nurr1 gain of function (OE) decreased Cnr1 transcript levels (p=0.0002), whereas it increased Gpld1 transcript levels (p=0.03). The transcript levels of these genes showed an opposite expression trend upon Nurr1 KD. D2L (p=0.008), but not D2S, Gatad2a (p=0.00001), Slc28a1 (p=0.0005) and Chit1 (p=0.02) showed significant expression profile changes only upon Nurr1 OE and vice versa.DiscussionNURR1 participates in developmental, differentiation and survival processes of dopaminergic neurons. It is implicated in transcriptional modulation of genes involved in dopaminergic transmission, including DRD2, as well as in behavioral phenotypes related to dopaminergic anomalies and reminiscent of SCZ in animal models. Finally, NURR1 genetic variation has been associated with SCZ. Taken together, our results are consistent with previous literature and with the hypothesis that molecular mechanisms responsible for co-expression in DRD2 network involve transcriptional regulation by NURR1. They also suggest reliability of our DRD2 co-expression network, and add new insights on mechanisms linked to DRD2-related molecular ensembles and to SCZ.

Diurnal and seasonal molecular rhythms in human neocortex and their relation to Alzheimer\u2019s disease

Circadian and seasonal rhythms are seen in many species, modulate several aspects of human physiology, including brain functions such as mood and cognition, and influence many neurological and psychiatric illnesses. However, there are few data regarding the genome-scale molecular correlates underlying these rhythms, especially in the human brain. Here, we report widespread, site-specific and interrelated diurnal and seasonal rhythms of gene expression in the human brain, and show their relationship with parallel rhythms of epigenetic modification including histone acetylation, and DNA methylation. We also identify transcription factor-binding sites that may drive these effects. Further, we demonstrate that Alzheimer’s disease pathology disrupts these rhythms. These data suggest that interrelated diurnal and seasonal epigenetic and transcriptional rhythms may be an important feature of human brain biology, and perhaps human biology more broadly, and that changes in such rhythms may be consequences of, or contributors to, diseases such as Alzheimer’s disease.

Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA “intrinsic properties” (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

An Integrated Approach for the Identification of HNF4α-Centered Transcriptional Regulatory Networks During Early Liver Regeneration

Background/Aims: Hepatocyte nuclear factor-4α (HNF4α), the liver enriched transcription factor (TF), is one of the major regulators of hepatocyte differentiation and proliferation. However, how HNF4α participate in liver regeneration after partial hepatectomy (PH) remains largely unknown. In order to identify the HNF4α-centered regulatory network, we applied an integrated analytic strategy, in which, TF array, mRNA microarray, bioinformatic analysis and ChIP-on-chip assays were integrated. Methods/Results: The TF signatures from MOUSE OATFA (TF-array) platform revealed that the activity of HNF4α was significantly reduced and 17 other TFs showed increased activity at 4 h post PH. Then the ChIP-on-chip analysis on HNF4α were combined with mRNA expression profiling to select the possible HNF4α target genes during early liver regeneration, which were then sub-grouped according to their signaling pathways. More specifically, the HNF4α target genes with the gene ontology (GO) terms of cytokine-cytokine receptor, Jak-STAT, MAPK, toll-like receptor and insulin signaling pathways were further analyzed with an advanced bioinformatics tool named oPOSSUM to identify TF binding sites occupancy and predict the co-regulatory relationship between TFs and targets. Furthermore, we identified that repressed HNF4α during the early phase of liver regeneration may contribute cooperatively to the induction of immediate early genes, such as, c-fos, c-jun and stat3. Conclusion: our data indicate that HNF4α may play an inhibitory role on the induction of specific promitogenic genes and liver regeneration initiation. The integrated approach of mRNA/OATFA/ChIP-DSL/oPOSSUM analysis may help us better characterize the target genes and co-regulatory network of HNF4α during the early stage of liver regeneration.

Open chromatin mapping identifies transcriptional networks regulating human epididymis epithelial function

The epithelium lining the epididymis in the male reproductive tract maintains a luminal environment that promotes sperm cell maturation. This process is dependent on the coordinated expression of many genes that encode proteins with a role in epithelial transport. We previously generated genome-wide maps of open chromatin in primary human epididymis epithelial (HEE) cells to identify potential regulatory elements controlling coordinated gene expression in the epididymis epithelium. Subsequent in silico analysis identified transcription factor-binding sites (TFBS) that were over-represented in the HEE open chromatin, including the motif for paired box 2 (PAX2). PAX2 is a critical transcriptional regulator of urogenital tract development, which has been well studied in the kidney but is unexplored in the epididymis. Due to the limited lifespan of primary HEE cells in culture, we investigated the role of PAX2 in an immortalized HEE cell line (REP). First, REP cells were evaluated by DNase I digestion followed by high-throughput sequencing and the PAX2-binding motif was again identified as an over-represented TFBS within intergenic open chromatin, though on fewer chromosomes than in the primary HEE cells. To identify PAX2-target genes in REP cells, RNA-seq analysis was performed after siRNA-mediated depletion of PAX2 and compared with that with a non-targeting siRNA. In response to PAX2-repression, 3135 transcripts were differentially expressed (1333 up-regulated and 1802 down-regulated). Novel PAX2 targets included multiple genes encoding proteins with predicted functions in the epididymis epithelium.

Abstract 46: Differential Gene and Transcription Factor Binding Site Expression Between the Intima and Media Drives Intimal Hyperplasia

Background: Vein grafts (VG) fail due to rapid intimal expansion in the weeks to months following implantation. Histologically, this results in a metabolically active intima juxtaposed to a quiescent medial layer. In this study, we leverage this dichotomy to identify the key genes that control intimal growth and the critical transcription factors (TF) that drive their activation. Methods: Microsurgical techniques were used to separate the intima and media of rabbit carotid VGs harvested at 1 (n=4), 3 (n=6), and 6 (n=4) months (m) following implantation. Each layer was independently analyzed for differential mRNA expression using a whole-genome microarray. rVista and TRANSFAC were used to identify TF binding sites within 5000 bp of differentially expressed genes. Results: Early growth of the intima was driven by an enhanced proliferative response and an accumulation of cell mass (Fig). During this early hyperplastic phase, substantial differences in intimal and medial gene expression were observed (197 genes at 1m and 346 at 3m, p<0.001) that resolved by 6m (8 genes). Genes involved in cell cycle regulation and cellular movement dominated the 1 and 3m differences (Fig). TF binding site analysis of up-regulated intimal genes at 1 month revealed enrichment of ETS and HOX, while CREB and bHLH-ZIP were the most over-represented among up-regulated 3m intimal genes. In the medial layer, RXR and zinc finger TF binding sites were most prominent among up-regulated genes at 1 and 3m, respectively. Conclusion: Corresponding to the most active phase of intimal growth, smooth muscle cells demonstrated marked up-regulation of an array of genes that modulate cell proliferation and migration, with these genes under the control of a discrete set of TFs. While previous attempts at modifying TF binding (via an E2F decoy) in the initial days following VG implantation have been unsuccessful, manipulation of a broader set of TFs in the time frame of weeks to months may prove a more promising strategy.

Differential expression of transcription factors predicted to regulate angiopoietin-1 and -2 genes is associated with specific angiopoietin signatures in RA and PsA synovial tissue

The angiogenic factors angiopoietin (Ang)-1 and -2 contribute to inflammation and are differentially expressed in synovial tissue of patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA). The underlying mechanisms for promoting differential expression are unknown. Here, we identified which cells express Ang-1 and Ang-2 in synovial tissue, transcription factors (TFs) predicted to regulate Ang-1 and Ang-2 transcription, and examined expression of these TFs in RA and PsA synovial tissue. Immunofluorescent double stainings of RA and PsA synovial tissue were performed with CD68, CD163, CD3, CD22 and vWF markers, in combination with antibodies against Ang-1 and Ang-2, using confocal imaging. In silico analysis was conducted to identify TF binding sites in the Ang-1 and Ang-2 promoters, using four different TF binding site prediction programs. RA synovial expression of TFs, Ang-1 and Ang-2 was examined in publicly available gene expression data sets. Candidate TFs were characterized by real-time PCR (qPCR). Ang-1 production was detected in CD68 and CD163 –positive macrophage, CD3-positive T lymphocytes, and CD22-positive B cells, while Ang-2 expression was restricted to vWF- positive endothelial cells, as well as macrophages. In silico studies identified 34 TFs that were predicted to regulate Ang-1 expression and 22 TFs which are involved in Ang-2 expression and are upregulated in RA synovial tissue (P< 0.05). Expression of these TF was readily detected by qPCR in RA and PsA synovial tissue biopsies. Our studies suggest that expression of Ang-1 and Ang-2 by different cell types could additional underlie the previously observed specific Ang-1 high signature in RA, compared to PsA. Enhanced expression of Ang-1 and Ang-2 in RA and PsA is strongly associated with expression of specific TFs, and understanding the mechanisms leading to the differential expression of these TFs may give insight into the etiology of RA and PsA.

Functional pathway mapping analysis for hypoxia-inducible factors

BackgroundHypoxia-inducible factors (HIFs) are transcription factors that play a crucial role in response to hypoxic stress in living organisms. The HIF pathway is activated by changes in cellular oxygen levels and has significant impacts on the regulation of gene expression patterns in cancer cells. Identifying functional conservation across species and discovering conserved regulatory motifs can facilitate the selection of reference species for empirical tests. This paper describes a cross-species functional pathway mapping strategy based on evidence of homologous relationships that employs matrix-based searching techniques for identifying transcription factor-binding sites on all retrieved HIF target genes.ResultsHIF-related orthologous and paralogous genes were mapped onto the conserved pathways to indicate functional conservation across species. Quantitatively measured HIF pathways are depicted in order to illustrate the extent of functional conservation. The results show that in spite of the evolutionary process of speciation, distantly related species may exhibit functional conservation owing to conservative pathways. The novel terms OrthRate and ParaRate are proposed to quantitatively indicate the flexibility of a homologous pathway and reveal the alternative regulation of functional genes.ConclusionThe developed functional pathway mapping strategy provides a bioinformatics approach for constructing biological pathways by highlighting the homologous relationships between various model species. The mapped HIF pathways were quantitatively illustrated and evaluated by statistically analyzing their conserved transcription factor-binding elements.Keywordshypoxia-inducible factor (HIF), hypoxia-response element (HRE), transcription factor (TF), transcription factor binding site (TFBS), KEGG (Kyoto Encyclopedia of Genes and Genomes), cross-species comparison, orthology, paralogy, functional pathway

Genomic Selection Identifies Vertebrate Transcription Factor Fezf2 Binding Sites and Target Genes

Identification of transcription factor targets is critical to understanding gene regulatory networks. Here, we uncover transcription factor binding sites and target genes employing systematic evolution of ligands by exponential enrichment (SELEX). Instead of selecting randomly synthesized DNA oligonucleotides as in most SELEX studies, we utilized zebrafish genomic DNA to isolate fragments bound by Fezf2, an evolutionarily conserved gene critical for vertebrate forebrain development. This is, to our knowledge, the first time that SELEX is applied to a vertebrate genome. Computational analysis of bound genomic fragments predicted a core consensus binding site, which identified response elements that mediated Fezf2-dependent transcription both in vitro and in vivo. Fezf2-bound fragments were enriched for conserved sequences. Surprisingly, ∼20% of these fragments overlapped well annotated protein-coding exons. Through loss of function, gain of function, and chromatin immunoprecipitation, we further identified and validated eomesa/tbr2 and lhx2b as biologically relevant target genes of Fezf2. Mutations in eomesa/tbr2 cause microcephaly in humans, whereas lhx2b is a critical regulator of cell fate and axonal targeting in the developing forebrain. These results demonstrate the feasibility of employing genomic SELEX to identify vertebrate transcription factor binding sites and target genes and reveal Fezf2 as a transcription activator and a candidate for evaluation in human microcephaly.

Finding subtypes of transcription factor motif pairs with distinct regulatory roles

DNA sequences bound by a transcription factor (TF) are presumed to contain sequence elements that reflect its DNA binding preferences and its downstream-regulatory effects. Experimentally identified TF binding sites (TFBSs) are usually similar enough to be summarized by a ‘consensus’ motif, representative of the TF DNA binding specificity. Studies have shown that groups of nucleotide TFBS variants (subtypes) can contribute to distinct modes of downstream regulation by the TF via differential recruitment of cofactors. A TFA may bind to TFBS subtypes a1 or a2 depending on whether it associates with cofactors TFB or TFC, respectively. While some approaches can discover motif pairs (dyads), none address the problem of identifying ‘variants’ of dyads. TFs are key components of multiple regulatory pathways targeting different sets of genes perhaps with different binding preferences. Identifying the discriminating TF–DNA associations that lead to the differential downstream regulation is thus essential. We present DiSCo (Discovery of Subtypes and Cofactors), a novel approach for identifying variants of dyad motifs (and their respective target sequence sets) that are instrumental for differential downstream regulation. Using both simulated and experimental datasets, we demonstrate how current motif discovery can be successfully leveraged to address this question.

The mouse and human Fli1 genes are similarly regulated by Ets factors in T cells

Fli1 is a member of the Ets family of transcription factors and is preferentially expressed in hematopoietic cell lineages. Its expression level is linked to the pathogenesis of lupus. In this study, we identified mechanisms involved in the transcriptional regulation of the mouse and human Fli1 promoters. We show that the Fli1 promoter is upregulated by Ets factors Ets1, Ets2, Fli1 and Elf1 either alone or in combination with GATA factors, but is inhibited by Tel. In vitro binding studies show that Elf1, Tel and Fli1 in T cells bind the three Ets-binding sites in the murine Fli1 proximal promoter. We identified transcription factor-binding sites in the human Fli1 promoter region that function in T cells in a similar manner to those in the mouse promoter. Furthermore, we show similar binding of Ets factors to the endogenous mouse and human Fli1 promoters in T cells and knocking down Ets1 results in an upregulation of Fli1 expression. Together, these results suggest that the human and mouse genes are regulated similarly and that Ets1 may be important in preventing the overexpression of Fli1 in T cells. This report lays the groundwork for identifying targets for manipulating Fli1 expression as a possible therapeutic approach.

An Integrated Approach to Identifying Cis-Regulatory Modules in the Human Genome

In eukaryotic genomes, it is challenging to accurately determine target sites of transcription factors (TFs) by only using sequence information. Previous efforts were made to tackle this task by considering the fact that TF binding sites tend to be more conserved than other functional sites and the binding sites of several TFs are often clustered. Recently, ChIP-chip and ChIP-sequencing experiments have been accumulated to identify TF binding sites as well as survey the chromatin modification patterns at the regulatory elements such as promoters and enhancers. We propose here a hidden Markov model (HMM) to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs). We conducted ChIP-chip experiments on four TFs, CREB, E2F1, MAX, and YY1 in 1% of the human genome. We then trained a hidden Markov model (HMM) to identify the labels of the CRMs by incorporating the sequence motifs recognized by these TFs and the ChIP-chip ratio. Chromatin modification data was used to predict the functional sites and to further remove false positives. Cross-validation showed that our integrated HMM had a performance superior to other existing methods on predicting CRMs. Incorporating histone signature information successfully penalized false prediction and improved the whole performance. The dataset we used and the software are available at http://nash.ucsd.edu/CIS/.

Genomics of Ischemia

The quest to understand the neurobiology of ischemic brain injury has relied on the investigational techniques available over the years. Historically, morbid anatomy was the approach used through the midportion of the last century.1 The establishment of the Journal of Neurochemisty in 1956 marks the assent of tools to understanding brain injury beyond the whole organ level. Cellular and molecular approaches supervened especially after the discovery of apoptosis or programmed cell death by Kerr and colleagues in 1972,2 which lead to a focus on cellular and molecular cell death mechanisms. We now know that ischemic cell death is accompanied by transcriptional and translational events that may modulate ischemic injury. The induction of death-inducing gene products, such as the caspases, and the regulation of prosurvival gene products, such as antiapoptotic bcl-2 family genes, are now well known.3 More broad-based approaches to gene modulation in disease, such as …