Wide-scale analysis of human functional transcription factor binding reveals a strong bias towards the transcription start site

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

MEPP: more transparent motif enrichment by profiling positional correlations

Score-based motif enrichment analysis (MEA) is typically applied to regulatory DNA to infer transcription factors (TFs) that may modulate transcription and chromatin state in different conditions. Most MEA methods determine motif enrichment independent of motif position within a sequence, even when those sequences harbor anchor points that motifs and their bound TFs may functionally interact with in a distance-dependent fashion, such as other TF binding motifs, transcription start sites (TSS), sequencing assay cleavage sites, or other biologically meaningful features. We developed motif enrichment positional profiling (MEPP), a novel MEA method that outputs a positional enrichment profile of a given TF's binding motif relative to key anchor points (e.g. transcription start sites, or other motifs) within the analyzed sequences while accounting for lower-order nucleotide bias. Using transcription initiation and TF binding as test cases, we demonstrate MEPP's utility in determining the sequence positions where motif presence correlates with measures of biological activity, inferring positional dependencies of binding site function. We demonstrate how MEPP can be applied to interpretation and hypothesis generation from experiments that quantify transcription initiation, chromatin structure, or TF binding measurements. MEPP is available for download from https://github.com/npdeloss/mepp.

BLSSpeller to discover novel regulatory motifs in maize

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

LncRNA ZNF582-AS1 Expression and Methylation in Breast Cancer and Its Biological and Clinical Implications

Background: Long non-coding RNAs (lncRNAs) play an important role in cellular activities and functions, but our understanding of their involvement in cancer is limited. Methods: TCGA data on RNA expression and DNA methylation were analyzed for lncRNAs' association with breast cancer survival, using the Cox proportional hazard regression model. Fresh tumor samples and clinical information from 361 breast cancer patients in our study were used to confirm the TCGA finding on ZNF582-AS1. A RT-qPCR method was developed to measure ZNF582-AS1 expression. Survival associations with ZNF582-AS1 were verified with a meta-analysis. In silico predictions of molecular targets and cellular functions of ZNF582-AS1 were performed based on its molecular signatures and nucleotide sequences. Results:ZNF582-AS1 expression was lower in breast tumors than adjacent normal tissues. Low ZNF582-AS1 was associated with high-grade or ER-negative tumors. Patients with high ZNF582-AS1 had a lower risk of relapse and death. These survival associations were confirmed in a meta-analysis and remained significant after adjustment for tumor grade, disease stage, patient age, and hormone receptor status. Correlation analysis indicated the possible suppression of ZNF582-AS1 expression by promoter methylation. Bioinformatics interrogation of molecular signatures suggested that ZNF582-AS1 could suppress tumor cell proliferation via downregulating the HER2-mediated signaling pathway. Analysis of online data also suggested that HIF-1-related transcription factors could suppress ZNF582-AS1 expression, and the lncRNA might bind to hsa-miR-940, a known oncogenic miRNA in breast cancer. Conclusions: ZNF582-AS1 may play a role in suppressing breast cancer progression. Elucidating the lncRNA's function and regulation may improve our understanding of the disease.

Dissection of c-AMP Response Element Architecture by Using Genomic and Episomal Massively Parallel Reporter Assays

In eukaryotes, transcription factors (TFs) orchestrate gene expression by binding to TF-binding sites (TFBSs) and localizing transcriptional co-regulators and RNA polymerase II to cis-regulatory elements. However, we lack a basic understanding of the relationship between TFBS composition and their quantitative transcriptional responses. Here, we measured expression driven by 17,406 synthetic cis-regulatory elements with varied compositions of a model TFBS, the c-AMP response element (CRE) by using massively parallel reporter assays (MPRAs). We find CRE number, affinity, and promoter proximity largely determines expression. In addition, we observe expression modulation based on the spacing between CREs and CRE distance to the promoter, where expression follows a helical periodicity. Finally, we compare library expression between an episomal MPRA and a genomically integrated MPRA, where a single cis-regulatory element is assayed per cell at a defined locus. These assays largely recapitulate each other, although weaker, non-canonical CREs exhibit greater activity in a genomic context.

BACH family members regulate angiogenesis and lymphangiogenesis by modulating VEGFC expression

Angiogenesis and lymphangiogenesis are key processes during embryogenesis as well as under physiological and pathological conditions. Vascular endothelial growth factor C (VEGFC), the ligand for both VEGFR2 and VEGFR3, is a central lymphangiogenic regulator that also drives angiogenesis. Here, we report that members of the highly conserved BACH (BTB and CNC homology) family of transcription factors regulate VEGFC expression, through direct binding to its promoter. Accordingly, down-regulation of bach2a hinders blood vessel formation and impairs lymphatic sprouting in a Vegfc-dependent manner during zebrafish embryonic development. In contrast, BACH1 overexpression enhances intratumoral blood vessel density and peritumoral lymphatic vessel diameter in ovarian and lung mouse tumor models. The effects on the vascular compartment correlate spatially and temporally with BACH1 transcriptional regulation of VEGFC expression. Altogether, our results uncover a novel role for the BACH/VEGFC signaling axis in lymphatic formation during embryogenesis and cancer, providing a novel potential target for therapeutic interventions.

SalMotifDB: a tool for analyzing putative transcription factor binding sites in salmonid genomes

Background

Recently developed genome resources in Salmonid fish provides tools for studying the genomics underlying a wide range of properties including life history trait variation in the wild, economically important traits in aquaculture and the evolutionary consequences of whole genome duplications. Although genome assemblies now exist for a number of salmonid species, the lack of regulatory annotations are holding back our mechanistic understanding of how genetic variation in non-coding regulatory regions affect gene expression and the downstream phenotypic effects.

Results

We present SalMotifDB, a database and associated web and R interface for the analysis of transcription factors (TFs) and their cis-regulatory binding sites in five salmonid genomes. SalMotifDB integrates TF-binding site information for 3072 non-redundant DNA patterns (motifs) assembled from a large number of metazoan motif databases. Through motif matching and TF prediction, we have used these multi-species databases to construct putative regulatory networks in salmonid species. The utility of SalMotifDB is demonstrated by showing that key lipid metabolism regulators are predicted to regulate a set of genes affected by different lipid and fatty acid content in the feed, and by showing that our motif database explains a significant proportion of gene expression divergence in gene duplicates originating from the salmonid specific whole genome duplication.

Conclusions

SalMotifDB is an effective tool for analyzing transcription factors, their binding sites and the resulting gene regulatory networks in salmonid species, and will be an important tool for gaining a better mechanistic understanding of gene regulation and the associated phenotypes in salmonids. SalMotifDB is available at https://salmobase.org/apps/SalMotifDB.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6051-0) contains supplementary material, which is available to authorized users.

The role of NF-κB and Elk-1 in the regulation of mouse ADAM17 expression

ADAM17 is a cell membrane metalloproteinase responsible for the release of ectodomains of numerous proteins from the cell surface. Although ADAM17 is often overexpressed in tumours and at sites of inflammation, little is known about the regulation of its expression. Here we investigate the role of NF-κB and Elk-1 transcription factors and upstream signalling pathways, NF-κB and ERK1/2 in ADAM17 expression in mouse brain endothelial cells stimulated with pro-inflammatory factors (TNF, IL-1β, LPS) or a phorbol ester (PMA), a well-known stimulator of ADAM17 activity. Notably, NF-κB inhibitor, IKK VII, interfered with the IL-1β- and LPS-mediated stimulation of ADAM17 expression. Furthermore, Adam17 promoter contains an NF-κB binding site occupied by p65 subunit of NF-κB. The transient increase in Adam17 mRNA in response to PMA was strongly reduced by an inhibitor of ERK1/2 phosphorylation, U0126. Luciferase reporter assay with vectors encoding the ERK1/2 substrate, Elk-1, fused with constitutively activating or repressing domains, indicated Elk-1 involvement in Adam17 expression. The site-directed mutagenesis of potential Elk-1 binding sites pointed to four functional Elk-1 binding sites in Adam17 promoter. All in all, our results indicate that NF-κB and Elk-1 transcription factors via NF-κB and ERK1/2 signalling pathways contribute to the regulation of mouse Adam17 expression.

Summary: We show the involvement of ERK1/2 and NF-κB pathways in the stimulation of mouse Adam17 expression and determine functional Elk-1- and NF-κB binding sites in its promoter.

High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity

Transcription initiates at both coding and noncoding genomic elements, including mRNA and long noncoding RNA (lncRNA) core promoters and enhancer RNAs (eRNAs). However, each class has a different expression profile with lncRNAs and eRNAs being the most tissue specific. How these complex differences in expression profiles and tissue specificities are encoded in a single DNA sequence remains unresolved. Here, we address this question using computational approaches and massively parallel reporter assays (MPRA) surveying hundreds of promoters and enhancers. We find that both divergent lncRNA and mRNA core promoters have higher capacities to drive transcription than nondivergent lncRNA and mRNA core promoters, respectively. Conversely, intergenic lncRNAs (lincRNAs) and eRNAs have lower capacities to drive transcription and are more tissue specific than divergent genes. This higher tissue specificity is strongly associated with having less complex transcription factor (TF) motif profiles at the core promoter. We experimentally validated these findings by testing both engineered single-nucleotide deletions and human single-nucleotide polymorphisms (SNPs) in MPRA. In both cases, we observe that single nucleotides associated with many motifs are important drivers of promoter activity. Thus, we suggest that high TF motif density serves as a robust mechanism to increase promoter activity at the expense of tissue specificity. Moreover, we find that 22% of common SNPs in core promoter regions have significant regulatory effects. Collectively, our findings show that high TF motif density provides redundancy and increases promoter activity at the expense of tissue specificity, suggesting that specificity of expression may be regulated by simplicity of motif usage.

Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis

In this study, Leucaena leucocephala wood was pretreated with aqueous glycerol having H₂SO₄ as the catalyst. Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the process parameters, catalyst concentration (1-3%), duration (120-300 min) and temperature (100-150 °C). ANN gave more accurate predictions for total reducing sugar yield than RSM. ANN also had lower values for error functions. Severity index (SI) was calculated based on the temperature, duration and catalyst concentration. Increase in SI from 0.21 * 10³ to 2.06 * 10³ increased total reducing sugar (TRS) production from 39.97 g/kg to 321.8 g/kg. Further increase in SI reduced the TRS and this change positively correlates with the loss of cellulose content. Correlation analysis showed that severity index can also be used to describe pretreatment process.

Derivation and molecular characterization of pancreatic differentiated MODY1-iPSCs

Maturity onset diabetes of the young (MODY) is a hereditary form of diabetes mellitus presenting at childhood or adolescence, which eventually leads to pancreatic β-cells dysfunction. The underlying genetic basis of MODY disorders is haploinsufficiency, where loss-of-function mutations in a single allele cause the diabetic phenotype in heterozygous patients. MODY1 is a type of MODY disorder resulting from a mutation in the transcription factor hepatocyte nuclear factor 4 alpha (HNF4α). In order to establish a human based model to study MODY1, we generated patient-derived induced pluripotent stem cells (iPSCs). Differentiation of these pluripotent cells towards the pancreatic lineage enabled to evaluate the effects of the MODY1 mutation and its impact on endodermal and pancreatic cells. Analyzing the gene expression profiles of differentiated MODY1 cells, revealed the outcome of HNF4α haploinsufficiency on its targets. This molecular analysis suggests that the differential expression of HNF4α target genes in MODY1 is affected by the number of HNF4α binding sites, their distance from the transcription start site, and the number of other transcription factor binding sites. These features may help explain the molecular manifestations of haploinsufficiency in MODY1 disease.

Precise mapping of the transcription start sites of human microRNAs using DROSHA knockout cells

Background

The expression of microRNAs (miRNAs) is primarily regulated during their transcription. However, the transcriptional regulation of miRNA genes has not been studied extensively owing to the lack of sufficient information about the promoters and transcription start sites of most miRNAs.

Results

In this study, we identified the transcription start sites of human primary miRNAs (pri-miRNAs) using DROSHA knockout cells. DROSHA knockout resulted in increased accumulation of pri-miRNAs and facilitated the precise mapping of their 5′ end nucleotides using the rapid amplification of cDNA ends (RACE) technique. By analyzing the promoter region encompassing the transcription start sites of miRNAs, we found that the unrelated miRNAs in their sequences have many common elements in their promoters for binding the same transcription factors. Moreover, by analyzing intronic miRNAs, we also obtained comprehensive evidence that miRNA-harboring introns are spliced more slowly than other introns.

Conclusions

The precisely mapped transcription start sites of pri-miRNAs, and the list of transcription factors for pri-miRNAs regulation, will be valuable resources for future studies to understand the regulatory network of miRNAs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3252-7) contains supplementary material, which is available to authorized users.

A regulatory similarity measure using the location information of transcription factor binding sites in Saccharomyces cerevisiae

Background

Defining a measure for regulatory similarity (RS) of two genes is an important step toward identifying co-regulated genes. To date, transcription factor binding sites (TFBSs) have been widely used to measure the RS of two genes because transcription factors (TFs) binding to TFBSs in promoters is the most crucial and well understood step in gene regulation. However, existing TFBS-based RS measures consider the relation of a TFBS to a gene as a Boolean (either 'presence' or 'absence') without utilizing the information of TFBS locations in promoters.

Results

Functional TFBSs of many TFs in yeast are known to have a strong positional preference to occur in a small region in the promoters. This biological knowledge prompts us to develop a novel RS measure that exploits the TFBS location information. The performances of different RS measures are evaluated by the fraction of gene pairs that are co-regulated (validated by literature evidence) by at least one common TF under different RS scores. The experimental results show that the proposed RS measure is the best co-regulation indicator among the six compared RS measures. In addition, the co-regulated genes identified by the proposed RS measure are also shown to be able to benefit three co-regulation-based applications: detecting gene co-function, gene co-expression and protein-protein interactions.

Conclusions

The proposed RS measure provides a good indicator for gene co-regulation. Besides, its good performance reveals the importance of the location information in TFBS-based RS measures.

JMJD2A attenuation affects cell cycle and tumourigenic inflammatory gene regulation in lipopolysaccharide stimulated neuroectodermal stem cells

JMJD2A is a lysine trimethyl-specific histone demethylase that is highly expressed in a variety of tumours. The role of JMJD2A in tumour progression remains unclear. The objectives of this study were to identify JMJD2A-regulated genes and understand the function of JMJD2A in p53-null neuroectodermal stem cells (p53(-/-) NE-4Cs). We determined the effect of LPS as a model of inflammation in p53(-/-) NE-4Cs and investigated whether the epigenetic modifier JMJD2A alter the expression of tumourigenic inflammatory genes. Global gene expression was measured in JMJD2A knockdown (kd) p53(-/-) NE-4Cs and in LPS-stimulated JMJD2A-kd p53(-/-) NE-4C cells. JMJD2A attenuation significantly down-regulated genes were Cdca2, Ccnd2, Ccnd1, Crebbp, IL6rα, and Stat3 related with cell cycle, proliferation, and inflammatory-disease responses. Importantly, some tumour-suppressor genes including Dapk3, Timp2 and TFPI were significantly up-regulated but were not affected by silencing of the JMJD2B. Furthermore, we confirmed the attenuation of JMJD2A also down-regulated Cdca2, Ccnd2, Crebbp, and Rest in primary NSCs isolated from the forebrains of E15 embryos of C57/BL6J mice with effective p53 inhibitor pifithrin-α (PFT-α). Transcription factor (TF) motif analysis revealed known binding patterns for CDC5, MYC, and CREB, as well as three novel motifs in JMJD2A-regulated genes. IPA established molecular networks. The molecular network signatures and functional gene-expression profiling data from this study warrants further investigation as an effective therapeutic target, and studies to elucidate the molecular mechanism of JMJD2A-kd-dependent effects in neuroectodermal stem cells should be performed.

Genome-wide survey of tissue-specific microRNA and transcription factor regulatory networks in 12 tissues

Tissue-specific miRNAs (TS miRNA) specifically expressed in particular tissues play an important role in tissue identity, differentiation and function. However, transcription factor (TF) and TS miRNA regulatory networks across multiple tissues have not been systematically studied. Here, we manually extracted 116 TS miRNAs and systematically investigated the regulatory network of TF-TS miRNA in 12 human tissues. We identified 2,347 TF-TS miRNA regulatory relations and revealed that most TF binding sites tend to enrich close to the transcription start site of TS miRNAs. Furthermore, we found TS miRNAs were regulated widely by non-tissue specific TFs and the tissue-specific expression level of TF have a close relationship with TF-genes regulation. Finally, we describe TSmiR (http://bioeng.swjtu.edu.cn/TSmiR), a novel and web-searchable database that houses interaction maps of TF-TS miRNA in 12 tissues. Taken together, these observations provide a new suggestion to better understand the regulatory network and mechanisms of TF-TS miRNAs underlying different tissues.

Integrative analysis of many RNA-seq datasets to study alternative splicing

Alternative splicing is an important gene regulatory mechanism that dramatically increases the complexity of the proteome. However, how alternative splicing is regulated and how transcription and splicing are coordinated are still poorly understood, and functions of transcript isoforms have been studied only in a few limited cases. Nowadays, RNA-seq technology provides an exceptional opportunity to study alternative splicing on genome-wide scales and in an unbiased manner. With the rapid accumulation of data in public repositories, new challenges arise from the urgent need to effectively integrate many different RNA-seq datasets for study alterative splicing. This paper discusses a set of advanced computational methods that can integrate and analyze many RNA-seq datasets to systematically identify splicing modules, unravel the coupling of transcription and splicing, and predict the functions of splicing isoforms on a genome-wide scale.

Identifying functional transcription factor binding sites in yeast by considering their positional preference in the promoters

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

Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set

Cervical cancer, the malignant neoplasm of the cervix uteri is the second most common cancer among women worldwide and the top-most cancer in India. Several factors are responsible for causing cervical cancer, which alter the expression of oncogenic genes resulting in up or down-regulation of gene expression and inactivation of tumor-suppressor genes/gene products. Gene expression is regulated by interactions between transcription factors (TFs) and specific regulatory elements in the promoter regions of target genes. Thus, it is important to decipher and analyze TFs that bind to regulatory regions of diseased genes and regulate their expression. In the present study, computational methods involving the combination of gene expression data from microarray experiments and promoter sequence analysis of a curated gene set involved in the cervical cancer causation have been utilized for identifying potential regulatory elements. Consensus predictions of two approaches led to the identification of twelve TFs that might be crucial to the regulation of cervical cancer progression. Subsequently, TF enrichment and oncomine expression analysis suggested that the transcription factor family E2F played an important role for the regulation of genes involve in cervical carcinogenesis. Our results suggest that E2F possesses diagnostic/prognostic value and can act as a potential drug target in cervical cancer.

Decoding a signature-based model of transcription cofactor recruitment dictated by cardinal cis-regulatory elements in proximal promoter regions

Genome-wide maps of DNase I hypersensitive sites (DHSs) reveal that most human promoters contain perpetually active cis-regulatory elements between −150 bp and +50 bp (−150/+50 bp) relative to the transcription start site (TSS). Transcription factors (TFs) recruit cofactors (chromatin remodelers, histone/protein-modifying enzymes, and scaffold proteins) to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby, contributing to the overall regulation of transcription. However, the rules of TF-mediated cofactor recruitment to the −150/+50 bp promoter regions remain poorly understood. Here, we provide evidence for a general model in which a series of cis-regulatory elements (here termed ‘cardinal’ motifs) prefer acting individually, rather than in fixed combinations, within the −150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets. Subsequently, human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures. In this study, furthermore, we have focused on promoters containing the nuclear respiratory factor 1 (NRF1) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 (LSD1/KDM1A). This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells. Together, we propose that decoding a signature-based, expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription.

Human cells exploit different mechanisms to coordinate the expression of both protein-coding and non-coding RNAs. Elucidating these mechanisms is essential to understanding normal physiology and disease. In our attempt to identify new regulatory layers acting particularly at proximal promoters, we have computationally analyzed the genomic sequences located from −150 bp to +50 bp relative to the transcriptional start site (TSS), which are often at the center of ‘open’ chromatin regions in human promoters. We have confirmed the presence of a series of cis-regulatory elements (here referred to as ‘cardinal’ motifs) that show a strong preference for these short regions. Interestingly, these elements tend to act independently rather than in fixed combinations. Therefore, we propose that they confer unique regulatory features to the human promoter subsets that contain each of these particular elements. In agreement with this model, we have identified a large repertoire of preferential partnerships between transcription factors recognizing cardinal motifs and their associated proteins (cofactors), thus decoding a signature-based model that distinguishes distinctive regulatory types of promoters based on cardinal motifs. These signatures may underlie a new layer of transcriptional regulation to orchestrate coordinated gene expression in human promoters.

Genome-wide computational prediction and analysis of core promoter elements across plant monocots and dicots

Transcription initiation, essential to gene expression regulation, involves recruitment of basal transcription factors to the core promoter elements (CPEs). The distribution of currently known CPEs across plant genomes is largely unknown. This is the first large scale genome-wide report on the computational prediction of CPEs across eight plant genomes to help better understand the transcription initiation complex assembly. The distribution of thirteen known CPEs across four monocots (Brachypodium distachyon, Oryza sativa ssp. japonica, Sorghum bicolor, Zea mays) and four dicots (Arabidopsis thaliana, Populus trichocarpa, Vitis vinifera, Glycine max) reveals the structural organization of the core promoter in relation to the TATA-box as well as with respect to other CPEs. The distribution of known CPE motifs with respect to transcription start site (TSS) exhibited positional conservation within monocots and dicots with slight differences across all eight genomes. Further, a more refined subset of annotated genes based on orthologs of the model monocot (O. sativa ssp. japonica) and dicot (A. thaliana) genomes supported the positional distribution of these thirteen known CPEs. DNA free energy profiles provided evidence that the structural properties of promoter regions are distinctly different from that of the non-regulatory genome sequence. It also showed that monocot core promoters have lower DNA free energy than dicot core promoters. The comparison of monocot and dicot promoter sequences highlights both the similarities and differences in the core promoter architecture irrespective of the species-specific nucleotide bias. This study will be useful for future work related to genome annotation projects and can inspire research efforts aimed to better understand regulatory mechanisms of transcription.

Expression of the genes coding for plastidic acetyl-CoA carboxylase subunits is regulated by a location-sensitive transcription factor binding site

Plastidic acetyl-CoA carboxylase (ACCase) regulates the rate of fatty acid synthesis. This enzyme is composed of biotin carboxyl carrier protein (BCCP), biotin carboxylase (BC), and carboxyltransferase (CT), which consists of α and β subunits. Among these components, CTβ is encoded by the plastidic genome. In Arabidopsis, BC and CTα are each encoded by a single gene, and there are two genes for BCCP, BCCP1 and BCCP2. Promoter analysis revealed that the 5'-UTR containing the AW box is necessary for the expression of these genes in seeds and seedlings. The results indicated that there are other transcription factors besides WRI1 that bind to the AW box and regulate these genes in organs other than seeds. Although the AW boxes at 748 and 532 bp upstream from the transcription start sites (TSSs) of the BC and CTα genes, respectively, were not functional in seeds, the latter was functional in seedlings. In addition, when these AW boxes were moved to approximately 200 bp upstream from the TSS, they became active in seeds but not in seedlings. These results suggest that the distance from the TSS affects the function of the AW box, and the AW box alone is not sufficient for expression in seedlings. A comparison of the protein levels of BC, BCCP1, BCCP2 and CTβ between a wri1 mutant, a WRI1-overexpressing line and control plants showed that protein levels of BCCP2 and BC but not BCCP1 and CTβ are affected by WRI1. The results suggest that ACCase subunits are differentially regulated by WRI1.

Inferring chromatin-bound protein complexes from genome-wide binding assays

Genome-wide binding assays can determine where individual transcription factors bind in the genome. However, these factors rarely bind chromatin alone, but instead frequently bind to cis-regulatory elements (CREs) together with other factors thus forming protein complexes. Currently there are no integrative analytical approaches that can predict which complexes are formed on chromatin. Here, we describe a computational methodology to systematically capture protein complexes and infer their impact on gene expression. We applied our method to three human cell types, identified thousands of CREs, inferred known and undescribed complexes recruited to these CREs, and determined the role of the complexes as activators or repressors. Importantly, we found that the predicted complexes have a higher number of physical interactions between their members than expected by chance. Our work provides a mechanism for developing hypotheses about gene regulation via binding partners, and deciphering the interplay between combinatorial binding and gene expression.

Multiple transcription factor binding sites predict AID targeting in non-Ig genes

Aberrant targeting of the enzyme activation-induced cytidine deaminase (AID) results in the accumulation of somatic mutations in ≈ 25% of expressed genes in germinal center B cells. Observations in Ung(-/-) Msh2(-/-) mice suggest that many other genes efficiently repair AID-induced lesions, so that up to 45% of genes may actually be targeted by AID. It is important to understand the mechanisms that recruit AID to certain genes, because this mistargeting represents an important risk for genome instability. We hypothesize that several mechanisms combine to target AID to each locus. To resolve which mechanisms affect AID targeting, we analyzed 7.3 Mb of sequence data, along with the regulatory context, from 83 genes in Ung(-/-) Msh2(-/-) mice to identify common properties of AID targets. This analysis identifies three transcription factor binding sites (E-box motifs, along with YY1 and C/EBP-β binding sites) that may work together to recruit AID. Based on previous knowledge and these newly discovered features, a classification tree model was built to predict genome-wide AID targeting. Using this predictive model, we were able to identify a set of 101 high-interest genes that are likely targets of AID.

Genome-wide Identification of IRF1 Binding Sites Reveals Extensive Occupancy at Cell Death Associated Genes

IRF1 is a transcription factor involved in interferon signaling and has been shown to harbor tumor suppressor activity. In order to comprehensively identify pathways regulated by IRF1, we used chromatin immunoprecipitation followed by massive-parallel sequencing (ChIP-seq) to evaluate the gene targets of IRF1 genome-wide. We identified 17,416 total binding events in breast cancer cells. Functional categorization of the binding sites after IFN-gamma (interferon-gamma) treatment determined that ‘apoptosis’ or ‘cell death’ is the most enriched target process. Motif discovery analysis of the chromosomal regions bound by IRF1 identified a number of unique motifs correlated with apoptosis, DNA damage and immune processes. Analysis of GEO transcriptome data from IRF1-transduced cells or IFN-gamma treated fibroblasts indicates that IRF1-bound targets in IFN-treated cells are associated with a positive transcriptional response. Many of the enriched target genes from the expression analysis are associated with apoptosis. Importantly, this data indicates that a significant function of IRF1 is the regulation of anti-cancer apoptotic pathways and this reinforces IRF1’s role as a tumor suppressor.

Algorithm to identify frequent coupled modules from two-layered network series: application to study transcription and splicing coupling

Current network analysis methods all focus on one or multiple networks of the same type. However, cells are organized by multi-layer networks (e.g., transcriptional regulatory networks, splicing regulatory networks, protein-protein interaction networks), which interact and influence each other. Elucidating the coupling mechanisms among those different types of networks is essential in understanding the functions and mechanisms of cellular activities. In this article, we developed the first computational method for pattern mining across many two-layered graphs, with the two layers representing different types yet coupled biological networks. We formulated the problem of identifying frequent coupled clusters between the two layers of networks into a tensor-based computation problem, and proposed an efficient solution to solve the problem. We applied the method to 38 two-layered co-transcription and co-splicing networks, derived from 38 RNA-seq datasets. With the identified atlas of coupled transcription-splicing modules, we explored to what extent, for which cellular functions, and by what mechanisms transcription-splicing coupling takes place.

Cell-type specific and combinatorial usage of diverse transcription factors revealed by genome-wide binding studies in multiple human cells

Cell-type diversity is governed in part by differential gene expression programs mediated by transcription factor (TF) binding. However, there are few systematic studies of the genomic binding of different types of TFs across a wide range of human cell types, especially in relation to gene expression. In the ENCODE Project, we have identified the genomic binding locations across 11 different human cell types of CTCF, RNA Pol II (RNAPII), and MYC, three TFs with diverse roles. Our data and analysis revealed how these factors bind in relation to genomic features and shape gene expression and cell-type specificity. CTCF bound predominantly in intergenic regions while RNAPII and MYC preferentially bound to core promoter regions. CTCF sites were relatively invariant across diverse cell types, while MYC showed the greatest cell-type specificity. MYC and RNAPII co-localized at many of their binding sites and putative target genes. Cell-type specific binding sites, in particular for MYC and RNAPII, were associated with cell-type specific functions. Patterns of binding in relation to gene features were generally conserved across different cell types. RNAPII occupancy was higher over exons than adjacent introns, likely reflecting a link between transcriptional elongation and splicing. TF binding was positively correlated with the expression levels of their putative target genes, but combinatorial binding, in particular of MYC and RNAPII, was even more strongly associated with higher gene expression. These data illuminate how combinatorial binding of transcription factors in diverse cell types is associated with gene expression and cell-type specific biology.

Human transcription factor genes involved in neuronal development tend to have high GC content and CpG elements in the proximal promoter region

Transcription factors (TFs) play critical roles in the development of the nervous system, but the transcriptional regulatory mechanisms of these genes are poorly understood. Here we analyzed 5-kb of the 5' flanking genomic DNA sequences of 41 TF genes involved in neuronal development. The results showed that the TF genes tend to have higher GC contents in the proximal region and most of the TF genes have at least one proximal GC-rich (GC content > 60%) promoter with a CpG island. The promoter distribution analysis showed that the GC-poor promoters were sporadically distributed within the 5-kb flanking genomic sequence (FGS); however, more than half (37 of 70) of the GC-rich promoters were located in the proximal region between nucleotides -1 and -500. Luciferase assays showed that partial GC-rich promoters increased gene expression in SH-SY5Y cells and that CpG methylation repressed the promoter activity. This study suggests a potential general mechanism for regulation of TF expression.

Amplification of the 20q chromosomal arm occurs early in tumorigenic transformation and may initiate cancer

Duplication of chromosomal arm 20q occurs in prostate, cervical, colon, gastric, bladder, melanoma, pancreas and breast cancer, suggesting that 20q amplification may play a causal role in tumorigenesis. According to an alternative view, chromosomal imbalance is mainly a common side effect of cancer progression. To test whether a specific genomic aberration might serve as a cancer initiating event, we established an in vitro system that models the evolutionary process of early stages of prostate tumor formation; normal prostate cells were immortalized by the over-expression of human telomerase catalytic subunit hTERT, and cultured for 650 days till several transformation hallmarks were observed. Gene expression patterns were measured and chromosomal aberrations were monitored by spectral karyotype analysis at different times. Several chromosomal aberrations, in particular duplication of chromosomal arm 20q, occurred early in the process and were fixed in the cell populations, while other aberrations became extinct shortly after their appearance. A wide range of bioinformatic tools, applied to our data and to data from several cancer databases, revealed that spontaneous 20q amplification can promote cancer initiation. Our computational model suggests that 20q amplification induced deregulation of several specific cancer-related pathways including the MAPK pathway, the p53 pathway and Polycomb group factors. In addition, activation of Myc, AML, B-Catenin and the ETS family transcription factors was identified as an important step in cancer development driven by 20q amplification. Finally we identified 13 "cancer initiating genes", located on 20q13, which were significantly over-expressed in many tumors, with expression levels correlated with tumor grade and outcome suggesting that these genes induce the malignant process upon 20q amplification.

Wide-ranging functions of E2F4 in transcriptional activation and repression revealed by genome-wide analysis

The E2F family of transcription factors has important roles in cell cycle progression. E2F4 is an E2F family member that has been proposed to be primarily a repressor of transcription, but the scope of its binding activity and functions in transcriptional regulation is not fully known. We used ChIP sequencing (ChIP-seq) to identify around 16 000 E2F4 binding sites which potentially regulate 7346 downstream target genes with wide-ranging functions in DNA repair, cell cycle regulation, apoptosis, and other processes. While half of all E2F4 binding sites (56%) occurred near transcription start sites (TSSs), ∼20% of sites occurred more than 20 kb away from any annotated TSS. These distal sites showed histone modifications suggesting that E2F4 may function as a long-range regulator, which we confirmed by functional experimental assays on a subset. Overexpression of E2F4 and its transcriptional cofactors of the retinoblastoma (Rb) family and its binding partner DP-1 revealed that E2F4 acts as an activator as well as a repressor. E2F4 binding sites also occurred near regulatory elements for miRNAs such as let-7a and mir-17, suggestive of regulation of miRNAs by E2F4. Taken together, our genome-wide analysis provided evidence of versatile roles of E2F4 and insights into its functions.

A novel HMM-based method for detecting enriched transcription factor binding sites reveals RUNX3 as a potential target in pancreatic cancer biology

BACKGROUND

Pancreatic adenocarcinoma (PAC) is one of the most intractable malignancies. In order to search for potential new therapeutic targets, we relied on computational methods aimed at identifying transcription factor binding sites (TFBSs) over-represented in the promoter regions of genes differentially expressed in PAC. Though many computational methods have been implemented to accomplish this, none has gained overall acceptance or produced proven novel targets in PAC. To this end we have developed DEMON, a novel method for motif detection.

METHODOLOGY

DEMON relies on a hidden Markov model to score the appearance of sequence motifs, taking into account all potential sites in a promoter of potentially varying binding affinities. We demonstrate DEMON's accuracy on simulated and real data sets. Applying DEMON to PAC-related data sets identifies the RUNX family as highly enriched in PAC-related genes. Using a novel experimental paradigm to distinguish between normal and PAC cells, we find that RUNX3 mRNA (but not RUNX1 or RUNX2 mRNAs) exhibits time-dependent increases in normal but not in PAC cells. These increases are accompanied by changes in mRNA levels of putative RUNX gene targets.

CONCLUSIONS

The integrated application of DEMON and a novel differentiation system led to the identification of a single family member, RUNX3, which together with four of its putative targets showed a robust response to a differentiation stimulus in healthy cells, whereas this regulatory mechanism was absent in PAC cells, emphasizing RUNX3 as a promising target for further studies.

Short rare MUC6 minisatellites-5 alleles influence susceptibility to gastric carcinoma by regulating gene

The human MUC6 gene, which is reported to be expressed in the stomach and gall bladder, is clustered on chromosome 11p15.5 with other secreted mucins. In this study, the genomic structure of MUC6 has been analyzed and five VNTR (minisatellites; MS1-MS5) were identified. These minisatellites were analyzed in genomic DNA extracted from 1,103 controls, 470 gastric cancer patients, and multigenerational families. Five novel minisatellites were found to be polymorphic and transmitted through meiosis by Mendelian inheritance in families. We evaluated allelic variation in these minisatellites to determine if such variation affected the susceptibility to gastric cancer. A significant association (odds ratio [OR]=7.08) between short rare MUC6-MS5 alleles and relative risks were observed for gastric cancer (95% confidence interval [CI], 1.43-35.19; P=0.005). To investigate the function of minisatellite alleles of MUC6-MS5, we examined the effects on gene expression from luciferase reporters when inserted with minisatellites. Interestingly, when the shortest allele (7TR) was inserted in the promoter, the expression level decreased over 20-fold (P<0.001) in normal and cancer cell lines. Furthermore, the cancer-specific rare allele (TR8) also showed decreased expression levels in cancer cells. Therefore, we suggest that the short rare MUC6-MS5 alleles may be related to cancer development by the regulation of MUC6 expression.

Gene promoter evolution targets the center of the human protein interaction network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom⁺ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod⁺ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom⁺ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom⁺ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Identification and characterization of putative methylation targets in the MAOA locus using bioinformatic approaches

Monoamine oxidase A (MAO A) is an enzyme that catalyzes the oxidation of neurotransmitter amines. A functional polymorphism in the human MAOA gene (high- and low-MAOA) has been associated with distinct behavioral phenotypes. To investigate directly the biological mechanism whereby this polymorphism influences brain function, we recently measured the activity of the MAO A enzyme in healthy volunteers. When found no relationship between the individual's brain MAO A level and the MAOA genotype, we postulated that there are additional regulatory mechanisms that control the MAOA expression. Given that DNA methylation is linked to the regulation of gene expression, we hypothesized that epigenetic mechanisms factor into the MAOA expression. Our underplaying assumption was that the differences in an individual's genotype play a key role in the epigenetic potential of the MAOA locus and, consequently, determine the individual's level of MAO A activity in the brain. As a first step towards experimental validation of the hypothesis, we performed a comprehensive bioinformatic analysis aiming to interrogate genomic features and attributes of the MAOA locus that might modulate its epigenetic sensitivity. Major findings of our analysis are the following: (1) the extended MAOA regulatory region contains two CpG islands (CGIs), one of which overlaps with the canonical MAOA promoter and the other is located further upstream; both CGIs exhibit sensitivity to differential methylation. (2) The uVNTR's effect on the MAOA's transcriptional activity might have epigenetic nature: this polymorphic region resides within the MAOA's CGI and itself contains CpGs, thus, the number of repeating increments effectively changes the number of methylatable cytosines in the MAOA promoter. An array of in silico analyses (the nucleosome positioning, the physical properties of the local DNA, the clustering of transcription-factor binding sites) together with experimental data on histone modifications and Pol 2 sites and data from the RefSeq mRNA library suggest that the MAOA gene might have an alternative promoter. Based on our findings, we propose a regulatory mechanism for the human MAOA according to which the MAOA expression in vivo is executed by the generation of tissue-specific transcripts initiated from the alternative promoters (both CGI-associated) where transcriptional activation of a particular promoter is under epigenetic control.

A quantitative model of transcriptional regulation reveals the influence of binding location on expression

Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.

Gene expression is controlled, in large part, by regulatory proteins called transcription factors that bind specific sites in the genome. A major focus of molecular biology has been understanding how these transcription factors interact with the cell's transcriptional machinery, the genome, and with each other to turn genes' expression on and off in various physiological contexts. Previous approaches for modeling transcriptional regulation have focused on the complex combinatorial interactions between groups of transcription factors at regulatory sites, or on the specific activating or repressive functions of individual proteins. In this work, we present a new modeling framework and demonstrate that an equally important, and previously overlooked, consideration in predicting the effect that a regulatory site has on gene expression is simply its location relative to the transcription start site of nearby genes. Our results show that, in general, the closer a binding event is to a gene's transcription start site, the more it influences expression. We also show that considering the particular proteins bound at a regulatory site helps predict the expression of nearby genes. However, considering the sequence conservation level of these sites does not lead to more accurate predictions.

Modulation of the vitamin D3 response by cancer-associated mutant p53

The p53 gene is mutated in many human tumors. Cells of such tumors often contain abundant mutant p53 (mutp53) protein, which may contribute actively to tumor progression via a gain-of-function mechanism. We applied ChIP-on-chip analysis and identified the vitamin D receptor (VDR) response element as overrepresented in promoter sequences bound by mutp53. We report that mutp53 can interact functionally and physically with VDR. Mutp53 is recruited to VDR-regulated genes and modulates their expression, augmenting the transactivation of some genes and relieving the repression of others. Furthermore, mutp53 increases the nuclear accumulation of VDR. Importantly, mutp53 converts vitamin D into an antiapoptotic agent. Thus, p53 status can determine the biological impact of vitamin D on tumor cells.

RFX3 governs growth and beating efficiency of motile cilia in mouse and controls the expression of genes involved in human ciliopathies

Cilia are cellular organelles that play essential physiological and developmental functions in various organisms. They can be classified into two categories, primary cilia and motile cilia, on the basis of their axonemal architecture. Regulatory factor X (RFX) transcription factors have been shown to be involved in the assembly of primary cilia in Caenorhabditis elegans, Drosophila and mice. Here, we have taken advantage of a novel primary-cell culture system derived from mouse brain to show that RFX3 is also necessary for biogenesis of motile cilia. We found that the growth and beating efficiencies of motile cilia are impaired in multiciliated Rfx3(-/-) cells. RFX3 was required for optimal expression of the FOXJ1 transcription factor, a key player in the differentiation program of motile cilia. Furthermore, we demonstrate for the first time that RFX3 regulates the expression of axonemal dyneins involved in ciliary motility by binding directly to the promoters of their genes. In conclusion, RFX proteins not only regulate genes involved in ciliary assembly, but also genes that are involved in ciliary motility and that are associated with ciliopathies such as primary ciliary dyskinesia in humans.

A dynamic network of transcription in LPS-treated human subjects

BACKGROUND

Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.

RESULTS

In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.

CONCLUSION

Using NCA, we were able to build a network that accounted for between 8-11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.

OHMM: a Hidden Markov Model accurately predicting the occupancy of a transcription factor with a self-overlapping binding motif

Background

DNA sequence binding motifs for several important transcription factors happen to be self-overlapping. Many of the current regulatory site identification methods do not explicitly take into account the overlapping sites. Moreover, most methods use arbitrary thresholds and fail to provide a biophysical interpretation of statistical quantities. In addition, commonly used approaches do not include the location of a site with respect to the transcription start site (TSS) in an integrated probabilistic framework while identifying sites. Ignoring these features can lead to inaccurate predictions as well as incorrect design and interpretation of experimental results.

Results

We have developed a tool based on a Hidden Markov Model (HMM) that identifies binding location of transcription factors with preference for self-overlapping DNA motifs by combining the effects of their alternative binding modes. Interpreting HMM parameters as biophysical quantities, this method uses the occupancy probability of a transcription factor on a DNA sequence as the discriminant function, earning the algorithm the name OHMM: Occupancy via Hidden Markov Model. OHMM learns the classification threshold by training emission probabilities using unaligned sequences containing known sites and estimating transition probabilities to reflect site density in all promoters in a genome. While identifying sites, it adjusts parameters to model site density changing with the distance from the transcription start site. Moreover, it provides guidance for designing padding sequences in gel shift experiments. In the context of binding sites to transcription factor NF-κB, we find that the occupancy probability predicted by OHMM correlates well with the binding affinity in gel shift experiments. High evolutionary conservation scores and enrichment in experimentally verified regulated genes suggest that NF-κB binding sites predicted by our method are likely to be functional.

Conclusion

Our method deals specifically with identifying locations with multiple overlapping binding sites by computing the local occupancy of the transcription factor. Moreover, considering OHMM as a biophysical model allows us to learn the classification threshold in a principled manner. Another feature of OHMM is that we allow transition probabilities to change with location relative to the TSS. OHMM could be used to predict physical occupancy, and provides guidance for proper design of gel-shift experiments. Based upon our predictions, new insights into NF-κB function and regulation and possible new biological roles of NF-κB were uncovered.

Increased microRNA activity in human cancers

MicroRNAs (miRNAs) are small regulatory RNAs that act by blocking the translation and increasing the degradation of target transcripts. MiRNAs play a critical role in many biological processes including development and differentiation and many studies have shown that major changes in miRNA levels occur in cancer. Since miRNAs degrade target messages, we used this property to develop a novel computational method aimed at determining the actual biological activity of miRNAs using variations in gene expression. Using the method described here, we quantified miRNA activity in papillary thyroid carcinoma and breast cancer, and found a strong and distinctive signal of increased global miRNA activity, embedded in the pertaining gene expression measurements. Interestingly, we found that in these two cancers, miRNA activity is globally increased, and is associated with a global downregulation of miRNA target genes. This downreguation of miRNA regulated genes is particularly noticeable for genes carrying multiple target sites for miRNAs. Among the miRNA-repressed genes, we found a significant enrichment of known tumor suppressors, thereby suggesting that the increased miRNA activity was indeed tumorigenic.

A transcription factor affinity-based code for mammalian transcription initiation

The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5'-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.

Motif discovery in promoters of genes co-localized and co-expressed during myeloid cells differentiation

Genes co-expressed may be under similar promoter-based and/or position-based regulation. Although data on expression, position and function of human genes are available, their true integration still represents a challenge for computational biology, hampering the identification of regulatory mechanisms. We carried out an integrative analysis of genomic position, functional annotation and promoters of genes expressed in myeloid cells. Promoter analysis was conducted by a novel multi-step method for discovering putative regulatory elements, i.e. over-represented motifs, in a selected set of promoters, as compared with a background model. The combination of transcriptional, structural and functional data allowed the identification of sets of promoters pertaining to groups of genes co-expressed and co-localized in regions of the human genome. The application of motif discovery to 26 groups of genes co-expressed in myeloid cells differentiation and co-localized in the genome showed that there are more over-represented motifs in promoters of co-expressed and co-localized genes than in promoters of simply co-expressed genes (CEG). Motifs, which are similar to the binding sequences of known transcription factors, non-uniformly distributed along promoter sequences and/or occurring in highly co-expressed subset of genes were identified. Co-expressed and co-localized gene sets were grouped in two co-expressed genomic meta-regions, putatively representing functional domains of a high-level expression regulation.

Genome-scale study of the importance of binding site context for transcription factor binding and gene regulation

BACKGROUND

The rate of mRNA transcription is controlled by transcription factors that bind to specific DNA motifs in promoter regions upstream of protein coding genes. Recent results indicate that not only the presence of a motif but also motif context (for example the orientation of a motif or its location relative to the coding sequence) is important for gene regulation.

RESULTS

In this study we present ContextFinder, a tool that is specifically aimed at identifying cases where motif context is likely to affect gene regulation. We used ContextFinder to examine the role of motif context in S. cerevisiae both for DNA binding by transcription factors and for effects on gene expression. For DNA binding we found significant patterns of motif location bias, whereas motif orientations did not seem to matter. Motif context appears to affect gene expression even more than it affects DNA binding, as biases in both motif location and orientation were more frequent in promoters of co-expressed genes. We validated our results against data on nucleosome positioning, and found a negative correlation between preferred motif locations and nucleosome occupancy.

CONCLUSION

We conclude that the requirement for stable binding of transcription factors to DNA and their subsequent function in gene regulation can impose constraints on motif context.

Positional distribution of human transcription factor binding sites

We developed a method for estimating the positional distribution of transcription factor (TF) binding sites using ChIP-chip data, and applied it to recently published experiments on binding sites of nine TFs: OCT4, SOX2, NANOG, HNF1A, HNF4A, HNF6, FOXA2, USF1 and CREB1. The data were obtained from a genome-wide coverage of promoter regions from 8-kb upstream of the transcription start site (TSS) to 2-kb downstream. The number of target genes of each TF ranges from few hundred to several thousand. We found that for each of the nine TFs the estimated binding site distribution is closely approximated by a mixture of two components: a narrow peak, localized within 300-bp upstream of the TSS, and a distribution of almost uniform density within the tested region. Using Gene Ontology (GO) and Enrichment analysis, we were able to associate (for each of the TFs studied) the target genes of both types of binding with known biological processes. Most GO terms were enriched either among the proximal targets or among those with a uniform distribution of binding sites. For example, the three stemness-related TFs have several hundred target genes that belong to ‘development’ and ‘morphogenesis’ whose binding sites belong to the uniform distribution.

ConTra: a promoter alignment analysis tool for identification of transcription factor binding sites across species

Transcription factors (TFs) are key components in signaling pathways, and the presence of their binding sites in the promoter regions of DNA is essential for their regulation of the expression of the corresponding genes. Orthologous promoter sequences are commonly used to increase the specificity with which potentially functional transcription factor binding sites (TFBSs) are recognized and to detect possibly important similarities or differences between the different species. The ConTra (conserved TFBSs) web server provides the biologist at the bench with a user-friendly tool to interactively visualize TFBSs predicted using either TransFac (1) or JASPAR (2) position weight matrix libraries, on a promoter alignment of choice. The visualization can be preceded by a simple scoring analysis to explore which TFs are the most likely to bind to the promoter of interest. The ConTra web server is available at http://bioit.dmbr.ugent.be/ConTra/index.php.

Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets

We present a threefold contribution to the computational task of motif discovery, a key component in the effort of delineating the regulatory map of a genome: (1) We constructed a comprehensive large-scale, publicly-available compendium of transcription factor and microRNA target gene sets derived from diverse high-throughput experiments in several metazoans. We used the compendium as a benchmark for motif discovery tools. (2) We developed Amadeus, a highly efficient, user-friendly software platform for genome-scale detection of novel motifs, applicable to a wide range of motif discovery tasks. Amadeus improves upon extant tools in terms of accuracy, running time, output information, and ease of use and is the only program that attained a high success rate on the metazoan compendium. (3) We demonstrate that by searching for motifs based on their genome-wide localization or chromosomal distributions (without using a predefined target set), Amadeus uncovers diverse known phenomena, as well as novel regulatory motifs.

Nutrition-induced catch-up growth increases hypoxia inducible factor 1alpha RNA levels in the growth plate

Although catch-up growth is a well-known phenomenon, the local pathways at the epiphyseal growth plate that govern this process remain poorly understood. To study the mechanisms governing catch-up growth in the growth plate, we subjected prepubertal rats to 10 days of 40% food restriction, followed by a renewal of the regular food supply to induce catch-up growth. The animals were weighed daily, and their humeral length was measured at sacrifice. The proximal tibial epiphyseal growth plates (EGPs) were studied, and findings were compared with EGPs from animals fed ad libitum and animals under food restriction. The gene expression profile in the growth plates was examined using DNA microarrays, and the expression levels of selected genes were validated by real-time polymerase chain reaction. To localize gene expression in different growth plate zones, microdissection was used. Protein levels and localization were examined using immunohistochemistry. We showed that the expression level of 550 genes decreased during food restriction and increased during catch-up growth, starting already one day after refeeding. HIF-1alpha, as well as several of its downstream targets, was found among these genes. Immunohistochemistry showed a similar pattern for HIF-1alpha protein abundance. Additionally, HIF-1alpha mRNA and protein levels were higher in the proliferating than in the hypertrophic zone, and this distribution was unaffected by nutritional status. These findings indicate that nutrition has a profound effect on gene expression level during growth plate growth, and suggest an important role for HIF-1alpha in the growth plate and its response to nutritional manipulation.

Generic eukaryotic core promoter prediction using structural features of DNA

Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists.