TRANSFAC: an integrated system for gene expression regulation

A map of open chromatin in human pancreatic islets

Tissue-specific transcriptional regulation is central to human disease1. To identify regulatory DNA active in human pancreatic islets, we profiled chromatin by FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements)2–4 coupled with high-throughput sequencing. We identified ~80,000 open chromatin sites. Comparison of islet FAIRE-seq to five non-islet cell lines revealed ~3,300 physically linked clusters of islet-selective open chromatin sites, which typically encompassed single genes exhibiting islet-specific expression. We mapped sequence variants to open chromatin sites and found that rs7903146, a TCF7L2 intronic variant strongly associated with type 2 diabetes (T2D)5, is located in islet-selective open chromatin. We show that rs7903146 heterozygotes exhibit allelic imbalance in islet FAIRE signal, and that the variant alters enhancer activity, indicating that genetic variation at this locus acts in cis with local chromatin and regulatory changes. These findings illuminate the tissue-specific organization of cis-regulatory elements, and show that FAIRE-seq can guide identification of regulatory variants important for disease.

Expander: from expression microarrays to networks and functions

A major challenge in the analysis of gene expression microarray data is to extract meaningful biological knowledge out of the huge volume of raw data. Expander (EXPression ANalyzer and DisplayER) is an integrated software platform for the analysis of gene expression data, which is freely available for academic use. It is designed to support all the stages of microarray data analysis, from raw data normalization to inference of transcriptional regulatory networks. The microarray analysis described in this protocol starts with importing the data into Expander 5.0 and is followed by normalization and filtering. Then, clustering and network-based analyses are performed. The gene groups identified are tested for enrichment in function (based on Gene Ontology), co-regulation (using transcription factor and microRNA target predictions) or co-location. The results of each analysis step can be visualized in a number of ways. The complete protocol can be executed in approximately 1 h.

SoyDB: a knowledge database of soybean transcription factors

BACKGROUND

Transcription factors play the crucial rule of regulating gene expression and influence almost all biological processes. Systematically identifying and annotating transcription factors can greatly aid further understanding their functions and mechanisms. In this article, we present SoyDB, a user friendly database containing comprehensive knowledge of soybean transcription factors.

DESCRIPTION

The soybean genome was recently sequenced by the Department of Energy-Joint Genome Institute (DOE-JGI) and is publicly available. Mining of this sequence identified 5,671 soybean genes as putative transcription factors. These genes were comprehensively annotated as an aid to the soybean research community. We developed SoyDB - a knowledge database for all the transcription factors in the soybean genome. The database contains protein sequences, predicted tertiary structures, putative DNA binding sites, domains, homologous templates in the Protein Data Bank (PDB), protein family classifications, multiple sequence alignments, consensus protein sequence motifs, web logo of each family, and web links to the soybean transcription factor database PlantTFDB, known EST sequences, and other general protein databases including Swiss-Prot, Gene Ontology, KEGG, EMBL, TAIR, InterPro, SMART, PROSITE, NCBI, and Pfam. The database can be accessed via an interactive and convenient web server, which supports full-text search, PSI-BLAST sequence search, database browsing by protein family, and automatic classification of a new protein sequence into one of 64 annotated transcription factor families by hidden Markov models.

CONCLUSIONS

A comprehensive soybean transcription factor database was constructed and made publicly accessible at http://casp.rnet.missouri.edu/soydb/.

Upregulation of c-MYC in cis through a large chromatin loop linked to a cancer risk-associated single-nucleotide polymorphism in colorectal cancer cells

Genome-wide association studies have mapped many single-nucleotide polymorphisms (SNPs) that are linked to cancer risk, but the mechanism by which most SNPs promote cancer remains undefined. The rs6983267 SNP at 8q24 has been associated with many cancers, yet the SNP falls 335 kb from the nearest gene, c-MYC. We show that the beta-catenin-TCF4 transcription factor complex binds preferentially to the cancer risk-associated rs6983267(G) allele in colon cancer cells. We also show that the rs6983267 SNP has enhancer-related histone marks and can form a 335-kb chromatin loop to interact with the c-MYC promoter. Finally, we show that the SNP has no effect on the efficiency of chromatin looping to the c-MYC promoter but that the cancer risk-associated SNP enhances the expression of the linked c-MYC allele. Thus, cancer risk is a direct consequence of elevated c-MYC expression from increased distal enhancer activity and not from reorganization/creation of the large chromatin loop. The findings of these studies support a mechanism for intergenic SNPs that can promote cancer through the regulation of distal genes by utilizing preexisting large chromatin loops.

Data-fusion in clustering microarray data: balancing discovery and interpretability

While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and Gene Ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.

Functional profiling methods in cancer

The introduction of new high-throughput methodologies such as DNA microarrays constitutes a major breakthrough in cancer research. The unprecedented amount of data produced by such technologies has opened new avenues for interrogating living systems although, at the same time, it has demanded of the development of new data analytical methods as well as new strategies for testing hypotheses. A history of early successful applications in cancer boosted the use of microarrays and fostered further applications in other fields. Keeping the pace with these technologies, bioinformatics offers new solutions for data analysis and, what is more important, permits the formulation of a new class of hypotheses inspired in systems biology, more oriented to pathways or, in general, to modules of functionally related genes. Although these analytical methodologies are new, some options are already available and are discussed in this chapter.

MicroRNA promoter analysis

In this chapter, we present a brief overview of current knowledge about the promoters of plant microRNAs (miRNAs), and provide a step-by-step guide for predicting plant miRNA promoter elements using known transcription factor binding motifs. The approach to promoter element prediction is based on a carefully constructed collection of Positional Weight Matrices (PWMs) for known transcription factors (TFs) in Arabidopsis. A key concept of the method is to use scoring thresholds for potential binding sites that are appropriate to each individual transcription factor. While the procedure can be applied to search for Transcription Factor Binding Sites (TFBSs) in any pol-II promoter region, it is particularly practical for the case of plant miRNA promoters where upstream sequence regions and binding sites are not readily available in existing databases. The majority of the material described in this chapter is available for download at http://microrna.gr.

Mutations in the perforin gene can be linked to macrophage activation syndrome in patients with systemic onset juvenile idiopathic arthritis

OBJECTIVE

Macrophage activation syndrome (MAS) in systemic onset juvenile idiopathic arthritis (SoJIA) is considered to be an acquired form of familial haemophagocytic lymphohistiocytosis (fHLH). FHLH is an autosomal recessive disorder, characterized by diminished NK cell function and caused by mutations in the perforin gene (PRF1) in 20-50% of patients. Interestingly, SoJIA patients display decreased levels of perforin in NK cells and diminished NK cell function as well. Here, we analysed PRF1 and its putative promoter in SoJIA patients with or without a history of MAS.

METHODS

DNA of 56 SoJIA patients (41 Italian and 15 Dutch) was isolated. Of these, 15 (27%) had a confirmed history of MAS. We sequenced PRF1 and 1.5 kb of the 5'-upstream region. DNA sequence variations in the promoter region were functionally tested in transfection experiments using a human NK cell line.

RESULTS

We detected a previously undescribed sequence variation (-499 C > T) in the promoter of PRF1 in 18% of the SoJIA patients. However, transfection experiments did not show functional implications of this variation. Secondly, we found that 11 of 56 (20%) SoJIA patients were heterozygous for missense mutations in PRF1. In particular, we found a high prevalence of the Ala91Val mutation, a variant known to result in defective function of perforin. Interestingly, the prevalence of Ala91Val in SoJIA patients with a history of MAS (20%) was increased compared with SoJIA patients without MAS (9.8%). One SoJIA patient, heterozygous for Ala91Val, showed profound decreased perforin levels at the time of MAS.

CONCLUSIONS

These findings suggest that PRF1 mutations play a role in the development of MAS in SoJIA patients.

Transcription factor binding site detection through position cross-mutual information variability analysis

Regulatory sequence detection is a fundamental challenge in computational biology. One key process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can show binding to the same factor. This variability found in binding sequences increases the difficulty of their detection using computational algorithms. In this manuscript, a method for the detection of binding sites is proposed, based on the correlation between binding sequence positions through information theoretical measures. Efficiency values of the method are reported in the form of Receiver Operating Characteristic curves on the detection of different transcription factors of the Saccharomyces cerevisiae organism. We compare our results with other known motif detection Motif Discovery scan (MDscan).

Gene expression profiling in multiple sclerosis: a disease of the central nervous system, but with relapses triggered in the periphery?

The aetiology of multiple sclerosis (MS), an autoimmune demyelinating disease of the central nervous system (CNS), includes both genetic and environmental factors, but the pathogenesis is still incompletely known. We performed gene expression profiling on paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMCs) samples from 26 MS patients without immunomodulatory treatment, sampled in relapse or remission, and 18 controls using Human Genome U133 plus 2.0 arrays (Affymetrix). In the CSF, 939 probe sets detected differential expression in MS patients compared to controls, but none in PBMCs, confirming that CSF cells might mirror the disease processes. The regulation of selected transcripts in CSF of MS patients was confirmed by quantitative PCR. Unexpectedly however, when comparing MS patients in relapse to those in remission, 266 probe sets detected differential expression in PBMCs, but not in CSF cells, indicating the importance of events outside of the CNS in the triggering of relapse.

Genomic targets of the KRAB and SCAN domain-containing zinc finger protein 263

Half of all human transcription factors use C2H2 zinc finger domains to specify site-specific DNA binding and yet very little is known about their role in gene regulation. Based on in vitro studies, a zinc finger code has been developed that predicts a binding motif for a particular zinc finger factor (ZNF). However, very few studies have performed genome-wide analyses of ZNF binding patterns, and thus, it is not clear if the binding code developed in vitro will be useful for identifying target genes of a particular ZNF. We performed genome-wide ChIP-seq for ZNF263, a C2H2 ZNF that contains 9 finger domains, a KRAB repression domain, and a SCAN domain and identified more than 5000 binding sites in K562 cells. Our results suggest that ZNF263 binds to a 24-nt site that differs from the motif predicted by the zinc finger code in several positions. Interestingly, many of the ZNF263 binding sites are located within the transcribed region of the target gene. Although ZNFs containing a KRAB domain are thought to function mainly as transcriptional repressors, many of the ZNF263 target genes are expressed at high levels. To address the biological role of ZNF263, we identified genes whose expression was altered by treatment of cells with ZNF263-specific small interfering RNAs. Our results suggest that ZNF263 can have both positive and negative effects on transcriptional regulation of its target genes.

Interaction of intestinal and pancreatic transcription factors in the regulation of CFTR gene expression

The tissue-specific regulation of the cystic fibrosis transmembrane conductance regulator gene (CFTR) is coordinated by intronic and extragenic cis-acting elements that influence its transcriptional activity. The promoter apparently lacks sequences to drive cell type-specific expression. We previously identified a number of intronic elements that were associated with DNase I hypersensitive sites (DHS) and bound the hepatocyte nuclear factor 1 (HNF1) transcription factor. Moreover, we demonstrated the likely involvement of HNF1 in the regulation of CFTR expression in vivo. Here we investigate DHS in introns 16 and 17a of the CFTR gene, which are evident in intestinal and pancreatic cell lines, and determine the transcription factors that interact with these sites. Of particular interest were factors known to interact with HNF1 in coordinated expression of genes in the gastrointestinal tract. We demonstrate that though sequences within these DHS bind HNF1, CDX2, and PBX1 in vitro, only PBX1 show a robust in vivo interaction. These data contribute to our understanding of the complexity of cell-type-specific CFTR regulatory mechanisms.

Genetic variation in PARL influences mitochondrial content

Given their involvement in processes necessary for life, mitochondrial damage and subsequent dysfunction can lead to a wide range of human diseases. Previous studies of both animal models and humans have suggested that presenilins-associated rhomboid-like protein (PARL) is a key regulator of mitochondrial integrity and function, and plays a role in cellular apoptosis. As a surrogate measure of mitochondrial integrity, we previously measured mitochondrial content in a Caucasian population consisting of large extended pedigrees, with results highlighting a substantial genetic component to this trait. To assess the influence of variation in the PARL gene on mitochondrial content, we re-sequenced 6.5 kb of the gene, identifying 16 SNPs and genotyped these in 1,086 Caucasian individuals, distributed across 170 families. Statistical genetic analysis revealed that one promoter variant, T-191C, exhibited significant effects (after correction for multiple testing) on mitochondrial content levels. Comparison of the transcription factor binding characteristics of the T-191C promoter SNP by EMSA indicates preferential binding of nuclear factors to the T allele, suggesting functional variation in PARL expression. These results suggest that genetic variation within PARL influences mitochondrial abundance and integrity.

A systematic approach to identify functional motifs within vertebrate developmental enhancers

Uncovering the cis-regulatory logic of developmental enhancers is critical to understanding the role of non-coding DNA in development. However, it is cumbersome to identify functional motifs within enhancers, and thus few vertebrate enhancers have their core functional motifs revealed. Here we report a combined experimental and computational approach for discovering regulatory motifs in developmental enhancers. Making use of the zebrafish gene expression database, we computationally identified conserved non-coding elements (CNEs) likely to have a desired tissue-specificity based on the expression of nearby genes. Through a high throughput and robust enhancer assay, we tested the activity of approximately 100 such CNEs and efficiently uncovered developmental enhancers with desired spatial and temporal expression patterns in the zebrafish brain. Application of de novo motif prediction algorithms on a group of forebrain enhancers identified five top-ranked motifs, all of which were experimentally validated as critical for forebrain enhancer activity. These results demonstrate a systematic approach to discover important regulatory motifs in vertebrate developmental enhancers. Moreover, this dataset provides a useful resource for further dissection of vertebrate brain development and function.

BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors

Background

We have incorporated Bayesian model regularization with biophysical modeling of protein-DNA interactions, and of genome-wide nucleosome positioning to study protein-DNA interactions, using a high-throughput dataset. The newly developed method (BayesPI) includes the estimation of a transcription factor (TF) binding energy matrices, the computation of binding affinity of a TF target site and the corresponding chemical potential.

Results

The method was successfully tested on synthetic ChIP-chip datasets, real yeast ChIP-chip experiments. Subsequently, it was used to estimate condition-specific and species-specific protein-DNA interaction for several yeast TFs.

Conclusion

The results revealed that the modification of the protein binding parameters and the variation of the individual nucleotide affinity in either recognition or flanking sequences occurred under different stresses and in different species. The findings suggest that such modifications may be adaptive and play roles in the formation of the environment-specific binding patterns of yeast TFs and in the divergence of TF binding sites across the related yeast species.

HPD: an online integrated human pathway database enabling systems biology studies

Background

Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.

Results

We developed the Human Pathway Database (HPD) by integrating heterogeneous human pathway data that are either curated at the NCI Pathway Interaction Database (PID), Reactome, BioCarta, KEGG or indexed from the Protein Lounge Web sites. Integration of pathway data at syntactic, semantic, and schematic levels was based on a unified pathway data model and data warehousing-based integration techniques. HPD provides a comprehensive online view that connects human proteins, genes, RNA transcripts, enzymes, signaling events, metabolic reaction events, and gene regulatory events. At the time of this writing HPD includes 999 human pathways and more than 59,341 human molecular entities. The HPD software provides both a user-friendly Web interface for online use and a robust relational database backend for advanced pathway querying. This pathway tool enables users to 1) search for human pathways from different resources by simply entering genes/proteins involved in pathways or words appearing in pathway names, 2) analyze pathway-protein association, 3) study pathway-pathway similarity, and 4) build integrated pathway networks. We demonstrated the usage and characteristics of the new HPD through three breast cancer case studies.

Conclusion

HPD http://bio.informatics.iupui.edu/HPD is a new resource for searching, managing, and studying human biological pathways. Users of HPD can search against large collections of human biological pathways, compare related pathways and their molecular entity compositions, and build high-quality, expanded-scope disease pathway models. The current HPD software can help users address a wide range of pathway-related questions in human disease biology studies.

Assigning roles to protein mentions: the case of transcription factors

Transcription factors (TFs) play a crucial role in gene regulation, and providing structured and curated information about them is important for genome biology. Manual curation of TF related data is time-consuming and always lags behind the actual knowledge available in the biomedical literature. Here we present a machine-learning text mining approach for identification and tagging of protein mentions that play a TF role in a given context to support the curation process. More precisely, the method explicitly identifies those protein mentions in text that refer to their potential TF functions. The prediction features are engineered from the results of shallow parsing and domain-specific processing (recognition of relevant appearing in phrases) and a phrase-based Conditional Random Fields (CRF) model is used to capture the content and context information of candidate entities. The proposed approach for the identification of TF mentions has been tested on a set of evidence sentences from the TRANSFAC and FlyTF databases. It achieved an F-measure of around 51.5% with a precision of 62.5% using 5-fold cross-validation evaluation. The experimental results suggest that the phrase-based CRF model benefits from the flexibility to use correlated domain-specific features that describe the dependencies between TFs and other entities. To the best of our knowledge, this work is one of the first attempts to apply text-mining techniques to the task of assigning semantic roles to protein mentions.

Evaluation of gremlin 1 (GREM1) as a candidate susceptibility gene for albuminuria-related traits in Mexican Americans with type 2 diabetes mellitus

Several novel genes that are up-regulated in the kidney in diabetes have been identified including GREM1, which encodes gremlin 1. GREM1 maps to human chromosome 15q12, a region previously found to be linked to albumin to creatinine ratio (ACR) in Mexican Americans. The objective of this study is to investigate whether genetic variants in GREM1, a positional candidate gene, contribute to variation in ACR. By sequencing 32 individuals for both exons and 2-kilobase putative promoter region of GREM1, we identified 19 genetic variants including 5 in the promoter region and 13 in the 3' untranslated region. Of 19 polymorphisms identified, 13 polymorphisms were genotyped in the entire cohort (N = 670, 39 large families) either by restriction fragment length polymorphism or by TaqMan (Applied Biosystems, Foster City, CA) assays. Association analyses between the genotypes and ACR, type 2 diabetes mellitus, and related phenotypes were carried out using a measured genotype approach as implemented in the variance component analytical tools (SOLAR). Of the variants examined for association, none exhibited statistically significant association with ACR after accounting for the effects of covariates such as age, sex, diabetes, duration of diabetes, systolic blood pressure, and antihypertensive medications. However, 2 novel variants at the 3' untranslated region showed significant association with estimated glomerular filtration rate (P = .010 and P = .049) and body mass index (P = .013 and P = .019) after accounting for trait-specific covariate influences. Furthermore, a novel variant located in the promoter exhibited a significant association with systolic (P = .038) and diastolic blood pressure (P = .005) after adjusting for the effects of age, sex, diabetes, and antihypertensive medications. In conclusion, the variants examined at GREM1 are not significant contributors to variation in ACR in Mexican Americans, although they appear to minimally influence risk factors related to ACR.

W-ChIPMotifs: a web application tool for de novo motif discovery from ChIP-based high-throughput data

Summary: W-ChIPMotifs is a web application tool that provides a user friendly interface for de novo motif discovery. The web tool is based on our previous ChIPMotifs program which is a de novo motif finding tool developed for ChIP-based high-throughput data and incorporated various ab initio motif discovery tools such as MEME, MaMF, Weeder and optimized the significance of the detected motifs by using a bootstrap resampling statistic method and a Fisher test. Use of a randomized statistical model like bootstrap resampling can significantly increase the accuracy of the detected motifs. In our web tool, we have modified the program in two aspects: (i) we have refined the P-value with a Bonferroni correction; (ii) we have incorporated the STAMP tool to infer phylogenetic information and to determine the detected motifs if they are novel and known using the TRANSFAC and JASPAR databases. A comprehensive result file is mailed to users.

Availability:http://motif.bmi.ohio-state.edu/ChIPMotifs. Data used in the article may be downloaded from http://motif.bmi.ohio-state.edu/ChIPMotifs/examples.shtml.

Contact:victor.jin@osumc.edu

Meta-coexpression conservation analysis of microarray data: a "subset" approach provides insight into brain-derived neurotrophic factor regulation

Background

Alterations in brain-derived neurotrophic factor (BDNF) gene expression contribute to serious pathologies such as depression, epilepsy, cancer, Alzheimer's, Huntington and Parkinson's disease. Therefore, exploring the mechanisms of BDNF regulation represents a great clinical importance. Studying BDNF expression remains difficult due to its multiple neural activity-dependent and tissue-specific promoters. Thus, microarray data could provide insight into the regulation of this complex gene. Conventional microarray co-expression analysis is usually carried out by merging the datasets or by confirming the re-occurrence of significant correlations across datasets. However, co-expression patterns can be different under various conditions that are represented by subsets in a dataset. Therefore, assessing co-expression by measuring correlation coefficient across merged samples of a dataset or by merging datasets might not capture all correlation patterns.

Results

In our study, we performed meta-coexpression analysis of publicly available microarray data using BDNF as a "guide-gene" introducing a "subset" approach. The key steps of the analysis included: dividing datasets into subsets with biologically meaningful sample content (e.g. tissue, gender or disease state subsets); analyzing co-expression with the BDNF gene in each subset separately; and confirming co- expression links across subsets. Finally, we analyzed conservation in co-expression with BDNF between human, mouse and rat, and sought for conserved over-represented TFBSs in BDNF and BDNF-correlated genes. Correlated genes discovered in this study regulate nervous system development, and are associated with various types of cancer and neurological disorders. Also, several transcription factor identified here have been reported to regulate BDNF expression in vitro and in vivo.

Conclusion

The study demonstrates the potential of the "subset" approach in co-expression conservation analysis for studying the regulation of single genes and proposes novel regulators of BDNF gene expression.

Computational design and application of endogenous promoters for transcriptionally targeted gene therapy for rheumatoid arthritis

The promoter regions of genes that are differentially regulated in the synovial membrane during the course of rheumatoid arthritis (RA) represent attractive candidates for application in transcriptionally targeted gene therapy. In this study, we applied an unbiased computational approach to define proximal-promoters from a gene expression profiling study of murine experimental arthritis. Synovium expression profiles from progressing stages of collagen-induced arthritis (CIA) were classified into six distinct groups using k-means clustering. Using an algorithm based on local over-representation and comparative genomics, we identified putatively functional transcription factor-binding sites (TFBS) in TATA-dependent proximal-promoters. Applying a filter based on spacing between TATA box and transcription start site (TSS) combined with the presence of over-represented nuclear factor kappaB (NFkappaB), AP-1, or CCAAT/enhancer-binding protein beta (C/EBPbeta) sites, 382 candidate murine and human promoters were reduced to 66, corresponding to 45 genes. In vitro, 9 out of 10 computationally defined promoter regions conferred cytokine-inducible expression in murine cells and human synovial fibroblasts. Under these conditions, the serum amyloid A3 (Saa3) promoter showed the strongest transcriptional induction and strength. We applied this promoter for driving therapeutically efficacious levels of the interleukin-1 receptor antagonist (Il1rn) in a disease-regulated fashion. These results demonstrate the value of bioinformatics for guiding the selection of endogenous promoters for transcriptionally targeted gene therapy.

Hypothesis: sarcoidosis is a STAT1-mediated disease

Immunologic pathways involved in sarcoidosis pathogenesis are largely unknown. We hypothesized that patients with sarcoidosis have characteristic mRNA profiles. Microarray analysis of gene expression was done on peripheral blood (12 patients, 12 controls), lung (6 patients, 6 controls) and lymph node (8 patients, 5 controls). Comparing peripheral blood from patients with sarcoidosis to controls, 872 transcripts were upregulated and 1039 were downregulated at >1.5-fold change and a significant q value. Several transcripts associated with interferon and STAT1 were upregulated. Lung and lymph node analyses also showed dramatic increases in STAT1 and STAT1-regulated chemokines. Granulomas in lymph nodes of patients with sarcoidosis expressed abundant STAT1 and phosphorylated STAT1. STAT1 might play an important role in sarcoidosis. This novel hypothesis unites seemingly disparate observations with regard to sarcoidosis including implication of a casual role for interferons, a suspected infectious trigger, T(H)1 predominating lymphocytes in bronchoalveolar lavage, and the association with hypercalcemia.

Epigenetic changes during disease progression in a murine model of human chronic lymphocytic leukemia

Epigenetic alterations, including gain or loss of DNA methylation, are a hallmark of nearly every malignancy. Changes in DNA methylation can impact expression of cancer-related genes including apoptosis regulators and tumor suppressors. Because such epigenetic changes are reversible, they are being aggressively investigated as potential therapeutic targets. Here we use the Emu-TCL1 transgenic mouse model of chronic lymphocytic leukemia (CLL) to determine the timing and patterns of aberrant DNA methylation, and to investigate the mechanisms that lead to aberrant DNA methylation. We show that CLL cells from Emu-TCL1 mice at various stages recapitulate epigenetic alterations seen in human CLL. Aberrant methylation of promoter sequences is observed as early as 3 months of age in these animals, well before disease onset. Abnormally methylated promoter regions include binding sites for the transcription factor FOXD3. We show that loss of Foxd3 expression due to an NF-kappaB p50/p50:HDAC1 repressor complex occurs in TCL1-positive B cells before methylation. Therefore, specific transcriptional repression is an early event leading to epigenetic silencing of target genes in murine and human CLL. These results provide strong rationale for the development of strategies to target NF-kappaB components in CLL and potentially other B-cell malignancies.

Word-based characterization of promoters involved in human DNA repair pathways

Background

DNA repair genes provide an important contribution towards the surveillance and repair of DNA damage. These genes produce a large network of interacting proteins whose mRNA expression is likely to be regulated by similar regulatory factors. Full characterization of promoters of DNA repair genes and the similarities among them will more fully elucidate the regulatory networks that activate or inhibit their expression. To address this goal, the authors introduce a technique to find regulatory genomic signatures, which represents a specific application of the genomic signature methodology to classify DNA sequences as putative functional elements within a single organism.

Results

The effectiveness of the regulatory genomic signatures is demonstrated via analysis of promoter sequences for genes in DNA repair pathways of humans. The promoters are divided into two classes, the bidirectional promoters and the unidirectional promoters, and distinct genomic signatures are calculated for each class. The genomic signatures include statistically overrepresented words, word clusters, and co-occurring words. The robustness of this method is confirmed by the ability to identify sequences that exist as motifs in TRANSFAC and JASPAR databases, and in overlap with verified binding sites in this set of promoter regions.

Conclusion

The word-based signatures are shown to be effective by finding occurrences of known regulatory sites. Moreover, the signatures of the bidirectional and unidirectional promoters of human DNA repair pathways are clearly distinct, exhibiting virtually no overlap. In addition to providing an effective characterization method for related DNA sequences, the signatures elucidate putative regulatory aspects of DNA repair pathways, which are notably under-characterized.

Computational analysis of the interaction between transcription factors and the predicted secreted proteome of the yeast Kluyveromyces lactis

Background

Protein secretion is a cell translocation process of major biological and technological significance. The secretion and downstream processing of proteins by recombinant cells is of great commercial interest. The yeast Kluyveromyces lactis is considered a promising host for heterologous protein production. Because yeasts naturally do not secrete as many proteins as filamentous fungi, they can produce secreted recombinant proteins with few contaminants in the medium. An ideal system to address the secretion of a desired protein could be exploited among the native proteins in certain physiological conditions. By applying algorithms to the completed K. lactis genome sequence, such a system could be selected. To this end, we predicted protein subcellular locations and correlated the resulting extracellular secretome with the transcription factors that modulate the cellular response to a particular environmental stimulus.

Results

To explore the potential Kluyveromyces lactis extracellular secretome, four computational prediction algorithms were applied to 5076 predicted K. lactis proteins from the genome database. SignalP v3 identified 418 proteins with N-terminal signal peptides. From these 418 proteins, the Phobius algorithm predicted that 176 proteins have no transmembrane domains, and the big-PI Predictor identified 150 proteins as having no glycosylphosphatidylinositol (GPI) modification sites. WoLF PSORT predicted that the K. lactis secretome consists of 109 putative proteins, excluding subcellular targeting. The transcription regulators of the putative extracellular proteins were investigated by searching for DNA binding sites in their putative promoters. The conditions to favor expression were obtained by searching Gene Ontology terms and using graph theory.

Conclusion

A public database of K. lactis secreted proteins and their transcription factors are presented. It consists of 109 ORFs and 23 transcription factors. A graph created from this database shows 134 nodes and 884 edges, suggesting a vast number of relationships to be validated experimentally. Most of the transcription factors are related to responses to stress such as drug, acid and heat resistance, as well as nitrogen limitation, and may be useful for inducing maximal expression of potential extracellular proteins.

An integrated genome screen identifies the Wnt signaling pathway as a major target of WT1

WT1, a critical regulator of kidney development, is a tumor suppressor for nephroblastoma but in some contexts functions as an oncogene. A limited number of direct transcriptional targets of WT1 have been identified to explain its complex roles in tumorigenesis and organogenesis. In this study we performed genome-wide screening for direct WT1 targets, using a combination of ChIP-ChIP and expression arrays. Promoter regions bound by WT1 were highly G-rich and resembled the sites for a number of other widely expressed transcription factors such as SP1, MAZ, and ZNF219. Genes directly regulated by WT1 were implicated in MAPK signaling, axon guidance, and Wnt pathways. Among directly bound and regulated genes by WT1, nine were identified in the Wnt signaling pathway, suggesting that WT1 modulates a subset of Wnt components and responsive genes by direct binding. To prove the biological importance of the interplay between WT1 and Wnt signaling, we showed that WT1 blocked the ability of Wnt8 to induce a secondary body axis during Xenopus embryonic development. WT1 inhibited TCF-mediated transcription activated by Wnt ligand, wild type and mutant, stabilized beta-catenin by preventing TCF4 loading onto a promoter. This was neither due to direct binding of WT1 to the TCF binding site nor to interaction between WT1 and TCF4, but by competition of WT1 and TCF4 for CBP. WT1 interference with Wnt signaling represents an important mode of its action relevant to the suppression of tumor growth and guidance of development.

Learning global models of transcriptional regulatory networks from data

Organisms must continually adapt to changing cellular and environmental factors (e.g., oxygen levels) by altering their gene expression patterns. At the same time, all organisms must have stable gene expression patterns that are robust to small fluctuations in environmental factors and genetic variation. Learning and characterizing the structure and dynamics of Regulatory Networks (RNs), on a whole-genome scale, is a key problem in systems biology. Here, we review the challenges associated with inferring RNs in a solely data-driven manner, concisely discuss the implications and contingencies of possible procedures that can be used, specifically focusing on one such procedure, the Inferelator. Importantly, the Inferelator explicitly models the temporal component of regulation, can learn the interactions between transcription factors and environmental factors, and attaches a statistically meaningful weight to every edge. The result of the Inferelator is a dynamical model of the RN that can be used to model the time-evolution of cell state.

Functional assessment of time course microarray data

Motivation

Time-course microarray experiments study the progress of gene expression along time across one or several experimental conditions. Most developed analysis methods focus on the clustering or the differential expression analysis of genes and do not integrate functional information. The assessment of the functional aspects of time-course transcriptomics data requires the use of approaches that exploit the activation dynamics of the functional categories to where genes are annotated.

Methods

We present three novel methodologies for the functional assessment of time-course microarray data. i) maSigFun derives from the maSigPro method, a regression-based strategy to model time-dependent expression patterns and identify genes with differences across series. maSigFun fits a regression model for groups of genes labeled by a functional class and selects those categories which have a significant model. ii) PCA-maSigFun fits a PCA model of each functional class-defined expression matrix to extract orthogonal patterns of expression change, which are then assessed for their fit to a time-dependent regression model. iii) ASCA-functional uses the ASCA model to rank genes according to their correlation to principal time expression patterns and assess functional enrichment on a GSA fashion. We used simulated and experimental datasets to study these novel approaches. Results were compared to alternative methodologies.

Results

Synthetic and experimental data showed that the different methods are able to capture different aspects of the relationship between genes, functions and co-expression that are biologically meaningful. The methods should not be considered as competitive but they provide different insights into the molecular and functional dynamic events taking place within the biological system under study.

DISCOVER: a feature-based discriminative method for motif search in complex genomes

MOTIVATION

Identifying transcription factor binding sites (TFBSs) encoding complex regulatory signals in metazoan genomes remains a challenging problem in computational genomics. Due to degeneracy of nucleotide content among binding site instances or motifs, and intricate 'grammatical organization' of motifs within cis-regulatory modules (CRMs), extant pattern matching-based in silico motif search methods often suffer from impractically high false positive rates, especially in the context of analyzing large genomic datasets, and noisy position weight matrices which characterize binding sites. Here, we try to address this problem by using a framework to maximally utilize the information content of the genomic DNA in the region of query, taking cues from values of various biologically meaningful genetic and epigenetic factors in the query region such as clade-specific evolutionary parameters, presence/absence of nearby coding regions, etc. We present a new method for TFBS prediction in metazoan genomes that utilizes both the CRM architecture of sequences and a variety of features of individual motifs. Our proposed approach is based on a discriminative probabilistic model known as conditional random fields that explicitly optimizes the predictive probability of motif presence in large sequences, based on the joint effect of all such features.

RESULTS

This model overcomes weaknesses in earlier methods based on less effective statistical formalisms that are sensitive to spurious signals in the data. We evaluate our method on both simulated CRMs and real Drosophila sequences in comparison with a wide spectrum of existing models, and outperform the state of the art by 22% in F1 score.

AVAILABILITY AND IMPLEMENTATION

The code is publicly available at http://www.sailing.cs.cmu.edu/discover.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DBH2H: vertebrate head-to-head gene pairs annotated at genomic and post-genomic levels

DBH2H collects head-to-head (h2h) gene pairs identified from human, mouse, rat, chicken and fugu genomes, and distinguishes the ortholog mapping relationship among them. The gene pairs in DBH2H are annotated with sequential features including single nucleotide polymorphisms, CpG islands and transcription factor binding sites, as well as functional terms and genetic disorders. In addition, the expression correlation information based on 117 microarray datasets is included. By providing user-friendly access to these data, DBH2H represents a valuable resource for further analyses of this important gene arrangement in terms of transcriptional regulation mechanisms, evolutionary conservation, disease relevance, etc.

Database URL:http://lifecenter.sgst.cn/h2h/

Tracking transcription factor complexes on DNA using total internal reflectance fluorescence protein binding microarrays

We have developed a high-throughput protein binding microarray (PBM) assay to systematically investigate transcription regulatory protein complexes binding to DNA with varied specificity and affinity. Our approach is based on the novel coupling of total internal reflectance fluorescence (TIRF) spectroscopy, swellable hydrogel double-stranded DNA microarrays and dye-labeled regulatory proteins, making it possible to determine both equilibrium binding specificities and kinetic rates for multiple protein:DNA interactions in a single experiment. DNA specificities and affinities for the general transcription factors TBP, TFIIA and IIB determined by TIRF–PBM are similar to those determined by traditional methods, while simultaneous measurement of the factors in binary and ternary protein complexes reveals preferred binding combinations. TIRF–PBM provides a novel and extendible platform for multi-protein transcription factor investigation.

The transcriptional regulation and cell-specific expression of the MAPK-activated protein kinase MK5

The mitogen-activated protein kinase (MAPK) cascades regulate important cellular processes, including growth, differentiation, apoptosis, embryogenesis, motility and gene expression. Although MAPKs mostly appear to be constitutively expressed, the transcript levels of some MAPK-encoding genes increase upon treatment with specific stimuli. This applies to the MAPKactivated protein kinases MK2 and MK3. By contrast, the transcriptional regulation of the related MK5 has not yet been studied. The MK5 promoters of mouse, rat and human contain a plethora of putative transcription factor sites, and the spatio-temporal expression of MK5 suggests inducible transcription of the gene. We examined the transcription pattern of MK5 in different tissues, and studied the kinetics of MK5 expression at the transcriptional and/or translation level in PC12 cells exposed to arsenite, forskolin, KCl, lipopolysaccharide, spermine NONOate, retinoic acid, serum, phorbol ester, temperature shock, and vanadate. Cells exposed to forskolin display a transient increase in MK5 mRNA, despite their unaltered MK5 protein levels. The MK5 promoters of human, mouse and rat contain a cAMP-responsive element that binds the cAMPresponsive element-binding protein (CREB) in vitro. Luciferase reporter constructs containing an 850-base pair human MK5 promoter fragment encompassing the CRE showed a basal activity that was 10-fold higher than the corresponding construct in which the CRE motif was deleted. siRNA-mediated depletion of CREB had no effect on the endogenous MK5 protein levels. Several binding motifs for heat shock factor are dispersed in the mouse and rat promoter, and temperature shock transiently enhanced the MK5 transcript levels. None of the other tested stimuli had an effect on the MK5 mRNA or protein levels. Our results indicate an inducible regulation of MK5 transcription in response to specific stimuli. However, the MK5 protein levels remained unaffected by all the stimuli tested. There is still no explanation for the discrepancy between the increased mRNA and unchanged MK5 protein levels.

Cell survival from chemotherapy depends on NF-kappaB transcriptional up-regulation of coenzyme Q biosynthesis

BACKGROUND

Coenzyme Q (CoQ) is a lipophilic antioxidant that is synthesized by a mitochondrial complex integrated by at least ten nuclear encoded COQ gene products. CoQ increases cell survival under different stress conditions, including mitochondrial DNA (mtDNA) depletion and treatment with cancer drugs such as camptothecin (CPT). We have previously demonstrated that CPT induces CoQ biosynthesis in mammal cells.

METHODOLOGY/PRINCIPAL FINDINGS

CPT activates NF-kappaB that binds specifically to two kappaB binding sites present in the 5'-flanking region of the COQ7 gene. This binding is functional and induces both the COQ7 expression and CoQ biosynthesis. The inhibition of NF-kappaB activation increases cell death and decreases both, CoQ levels and COQ7 expression induced by CPT. In addition, using a cell line expressing very low of NF-kappaB, we demonstrate that CPT was incapable of enhancing enhance both CoQ biosynthesis and COQ7 expression in these cells.

CONCLUSIONS/SIGNIFICANCE

We demonstrate here, for the first time, that a transcriptional mechanism mediated by NF-kappaB regulates CoQ biosynthesis. This finding contributes new data for the understanding of the regulation of the CoQ biosynthesis pathway.

A census of human transcription factors: function, expression and evolution

Transcription factors are key cellular components that control gene expression: their activities determine how cells function and respond to the environment. Currently, there is great interest in research into human transcriptional regulation. However, surprisingly little is known about these regulators themselves. For example, how many transcription factors does the human genome contain? How are they expressed in different tissues? Are they evolutionarily conserved? Here, we present an analysis of 1,391 manually curated sequence-specific DNA-binding transcription factors, their functions, genomic organization and evolutionary conservation. Much remains to be explored, but this study provides a solid foundation for future investigations to elucidate regulatory mechanisms underlying diverse mammalian biological processes.

Identification of adjacent binding sites for the YY1 and E4BP4 transcription factors in the ovine PrP (Prion) gene promoter

The PrP gene encodes the cellular isoform of the prion protein (PrP(c)) which has been shown to be crucial to the development of transmissible spongiform encephalopathies (TSEs). PrP knock-out mice, which do not express endogenous PrP(c), exhibit resistance to TSE disease. The regulation of PrP gene expression represents, therefore, a crucial factor in the development of TSEs. Two sequence motifs in the PrP promoter (positions -287 to -263 from transcriptional start) were previously reported as being highly conserved, and it was suggested that they represent binding sites for as yet unidentified transcription factors. To test this hypothesis, binding of nuclear proteins was analyzed by electrophoretic mobility shift assays using ovine or murine cells and tissues with radiolabeled DNA probes containing the conserved motif sequences. Specific binding was observed to both motifs, and polymorphic variants of these motifs exhibited differential binding. Two proteins bound to these motifs were identified as the Yin Yang 1 (YY1) (motif 1) and E4BP4 (motif 2) transcription factors. Functional promoter analysis of four different promoter variants revealed that motif 1 (YY1) was associated with inhibitory activity in the context of the PrP promoter, whereas motif 2 (E4BP4) was linked to a slight enhancing activity. This represents the first demonstration of binding of nuclear factors to two highly conserved DNA sequence motifs within mammalian PrP promoters. The action of these factors on the PrP promoter is haplotype-specific, leading us to propose that the prion protein expression pattern and, with it, the distribution of TSE infectivity may be associated with PrP promoter genotype.

MicroRNA expression profiling during neural differentiation of mouse embryonic carcinoma P19 cells

MicroRNAs (or miRNAs) are small non-coding RNAs (21-25 nucleotides) that are involved in a wide range of activities related to the development and differentiation of cells. Comparison of the miRNA expression profiles of mouse P19 embryonic carcinoma cells with those of differentiated neural stem cells showed that the expression level of 65 miRNAs changed (2-fold) after differentiation. MiR-124a was dramatically upregulated (more than 20-fold) while miRNAs of the miR-302 family and those in the miR-290-295 cluster were strongly down-regulated. Further analysis revealed that some important factors such as Oct4 and Sox2 appeared to be involved in the regulation of these miRNAs. These results may contribute to a better understanding of miRNA-regulated neural differentiation in early mouse embryos.

Management of gene promoter mutations in molecular diagnostics

BACKGROUND

Although promoter mutations are known to cause functionally important consequences for gene expression, promoter analysis is not a regular part of DNA diagnostics.

CONTENT

This review covers different important aspects of promoter mutation analysis and includes a proposed model procedure for studying promoter mutations. Characterization of a promoter sequence variation includes a comprehensive study of the literature and databases of human mutations and transcription factors. Phylogenetic footprinting is also used to evaluate the putative importance of the promoter region of interest. This in silico analysis is, in general, followed by in vitro functional assays, of which transient and stable transfection assays are considered the gold-standard methods. Electrophoretic mobility shift and supershift assays are used to identify trans-acting proteins that putatively interact with the promoter region of interest. Finally, chromatin immunoprecipitation assays are essential to confirm in vivo binding of these proteins to the promoter.

SUMMARY

Although promoter mutation analysis is complex, often laborious, and difficult to perform, it is an essential part of the diagnosis of disease-causing promoter mutations and improves our understanding of the role of transcriptional regulation in human disease. We recommend that routine laboratories and research groups specialized in gene promoter research cooperate to expand general knowledge and diagnosis of gene-promoter defects.

Transcriptional signatures in response to wheat germ agglutinin and starvation in Drosophila melanogaster larval midgut

One function of plant lectins such as wheat germ agglutinin is to serve as defences against herbivorous insects. The midgut is one critical site affected by dietary lectins. We observed marked cellular, structural and gene expression changes in the midguts of Drosophila melanogaster third instar larvae that were fed wheat germ agglutinin. Some of these changes were similar to those observed in the midguts of starved D. melanogaster. Dietary wheat germ agglutinin caused shortening, branching, swelling, distortion and in some cases disintegration of the midgut microvilli. Starvation was accompanied primarily by shortening of the microvilli. Microarray analyses revealed that dietary wheat germ agglutinin evoked differential expression of 61 transcripts; seven of these were also differentially expressed in starved D. melanogaster. The differentially transcribed gene clusters in wheat germ agglutinin-fed larvae were associated with (1) cytoskeleton organization; (2) digestive enzymes; (3) detoxification reactions; and (4) energy metabolism. Four possible transcription factor binding motifs were associated with the differentially expressed genes. One of these exhibited substantial similarity to MyoD, a transcription factor binding motif associated with cellular structures in mammals. These results are consistent with the hypothesis that wheat germ agglutinin caused a starvation-like effect and structural changes of midgut cells of D. melanogaster third-instar larvae.

The contribution of cis-regulatory elements to head-to-head gene pairs' co-expression pattern

Transcription regulation is one of the most critical pipelines in biological process, in which cis-elements play the role as gene expression regulators. We attempt to deduce the principles underlying the co-expression of "head-to-head" gene pairs by analyzing activities or behaviors of the shared cis-elements. A network component analysis was performed to estimate the impact of cis-elements on gene promoters and their activities under different conditions. Our discoveries reveal how biological system uses those regulatory elements to control the expression pattern of "head-to-head" gene pairs and the whole transcription regulation system.

Genome-wide determination of DNA methylation by Hpa II tiny fragment enrichment by ligation-mediated PCR (HELP) for the study of acute leukemias

Aberrant distribution of cytosine methylation in cancer has been linked to deregulation of gene expression and genomic instability. DNA methylation changes in cancer include both hyper and hypomethylation, and the precise localization of these changes is directly related to the impact they have on gene regulation. To determine both the localization and extent of DNA methylation status under different conditions, we have developed the HpaII tiny fragment enrichment by ligation-mediated PCR (HELP) assay, a microarray-based technique that allows the simultaneous interrogation of the methylation status of hundreds of thousands of CpG dinucleotides. The HELP assay allows methylation levels throughout the genome to be accurately determined so that the epigenetic state of leukemia cells can be identified, compared, and contrasted.

Universal protein-binding microarrays for the comprehensive characterization of the DNA-binding specificities of transcription factors

Protein-binding microarray (PBM) technology provides a rapid, high-throughput means of characterizing the in vitro DNA-binding specificities of transcription factors (TFs). Using high-density, custom-designed microarrays containing all 10-mer sequence variants, one can obtain comprehensive binding-site measurements for any TF, regardless of its structural class or species of origin. Here, we present a protocol for the examination and analysis of TF-binding specificities at high resolution using such 'all 10-mer' universal PBMs. This procedure involves double-stranding a commercially synthesized DNA oligonucleotide array, binding a TF directly to the double-stranded DNA microarray and labeling the protein-bound microarray with a fluorophore-conjugated antibody. We describe how to computationally extract the relative binding preferences of the examined TF for all possible contiguous and gapped 8-mers over the full range of affinities, from highest affinity sites to nonspecific sites. Multiple proteins can be tested in parallel in separate chambers on a single microarray, enabling the processing of a dozen or more TFs in a single day.

Better estimation of protein-DNA interaction parameters improve prediction of functional sites

Background

Characterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method designed to produce many such sequences.

Results

We analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP), and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones) in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay) and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation) trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites.

Conclusion

We think that training binding site detection algorithms on datasets from binding assays lead to better prediction. The improvements in accuracy came from the unbiased nature of the SELEX dataset rather than from the number of sites available. We believe that with progress in short-read sequencing technology, one could use SELEX methods to characterize binding affinities of many low specificity transcription factors.

Independent transcription of miR-281 in the intron of ODA in Drosophila melanogaster

MicroRNAs (miRNAs) have recently received much interest for their role in post-transcriptional regulation. However, the study of miRNA transcription lags behind that of gene cloning and functional analysis. The MiR-281 in Drosophila melanogaster is located in the first intron of isoform RA of the ornithine decarboxylase antizyme (ODA) gene. ODA has three isoforms because of alternative transcription start sites (TSSs). Expression profile analysis indicated that miR-281 is not co-expressed with any ODA isoform. We amplified the primary transcripts of miR-281 using the RACE technique. The pri-miRNA is 2149bp with a poly (A) tail and a canonical polyadenylation signal (AATAAA). Chromatin immunoprecipitation analysis confirmed the binding of hypophosphorylated Pol-II and the transcription factor Myc at the core miR-281 promoter region. The abundance of miR-281 does not correlate, either positively or negatively, with the expression of any ODA isoform, indicating that ODA has little influence on the transcription of miR-281.