Mapping DNA-protein interactions in large genomes by sequence tag analysis of genomic enrichment

The transcriptional regulatory network modulating human trophoblast stem cells to extravillous trophoblast differentiation

During human pregnancy, extravillous trophoblasts play crucial roles in placental invasion into the maternal decidua and spiral artery remodeling. However, regulatory factors and their action mechanisms modulating human extravillous trophoblast specification have been unknown. By analyzing dynamic changes in transcriptome and enhancer profile during human trophoblast stem cell to extravillous trophoblast differentiation, we define stage-specific regulators, including an early-stage transcription factor, TFAP2C, and multiple late-stage transcription factors. Loss-of-function studies confirm the requirement of all transcription factors identified for adequate differentiation, and we reveal that the dynamic changes in the levels of TFAP2C are essential. Notably, TFAP2C pre-occupies the regulatory elements of the inactive extravillous trophoblast-active genes during the early stage of differentiation, and the late-stage transcription factors directly activate extravillous trophoblast-active genes, including themselves as differentiation further progresses, suggesting sequential actions of transcription factors assuring differentiation. Our results reveal stage-specific transcription factors and their inter-connected regulatory mechanisms modulating extravillous trophoblast differentiation, providing a framework for understanding early human placentation and placenta-related complications.

How low can you go? Pushing the limits of low-input ChIP-seq

In the past decade, chromatin immunoprecipitation sequencing (ChIP-seq) has emerged as the dominant technique for those wishing to perform genome-wide protein:DNA profiling. Owing to the tissue- and cell-type-specific nature of epigenetic marks, the field has been driven towards obtaining data from ever-lower cell numbers. In this review, we focus on the methodological developments that have lowered input requirements and the biological findings they have enabled, as we strive towards the ultimate goal of robust single-cell ChIP-seq.

A comparative study of ChIP-seq sequencing library preparation methods

Background

ChIP-seq is the primary technique used to investigate genome-wide protein-DNA interactions. As part of this procedure, immunoprecipitated DNA must undergo “library preparation” to enable subsequent high-throughput sequencing. To facilitate the analysis of biopsy samples and rare cell populations, there has been a recent proliferation of methods allowing sequencing library preparation from low-input DNA amounts. However, little information exists on the relative merits, performance, comparability and biases inherent to these procedures. Notably, recently developed single-cell ChIP procedures employing microfluidics must also employ library preparation reagents to allow downstream sequencing.

Results

In this study, seven methods designed for low-input DNA/ChIP-seq sample preparation (Accel-NGS® 2S, Bowman-method, HTML-PCR, SeqPlex™, DNA SMART™, TELP and ThruPLEX®) were performed on five replicates of 1 ng and 0.1 ng input H3K4me3 ChIP material, and compared to a “gold standard” reference PCR-free dataset. The performance of each method was examined for the prevalence of unmappable reads, amplification-derived duplicate reads, reproducibility, and for the sensitivity and specificity of peak calling.

Conclusions

We identified consistent high performance in a subset of the tested reagents, which should aid researchers in choosing the most appropriate reagents for their studies. Furthermore, we expect this work to drive future advances by identifying and encouraging use of the most promising methods and reagents. The results may also aid judgements on how comparable are existing datasets that have been prepared with different sample library preparation reagents.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3135-y) contains supplementary material, which is available to authorized users.

Regulation of gene expression: probing DNA–protein interactionsin vivoandin vitro

Tremendous efforts have been put together over the last several years to complete the entire sequencing of the human genome. As we enter the proteomic era, when the major aim is understanding which gene encodes which protein, the time has also come to identify their precise function inside the astonishing signaling network required to accomplish all cellular functions. Understanding when, why and how a gene is expressed has now become a necessity toward identifying all the regulatory pathways that mediate cellular processes such as differentiation, migration, replication, DNA repair and apoptosis. Regulation of gene transcription is a process that is primarily under the influence of nuclear-located transcription factors. Consequently, identifying which protein activates or represses a specific gene is a prerequisite for understanding cell fate and function. The current state of, and recent advances in, transcriptional regulation approaches are reviewed here, with special emphasis on new technologies required when probing for DNA-protein interactions. This review explores different strategies aimed at identifying both the regulatory sequences of any given gene and the trans-acting regulatory factors that recognize these elements as their target sites in the nucleus. Ongoing developments in the fields of nanotechnology, RNA silencing and protein modeling toward the investigation of DNA-protein interactions and their relevance in the battle against cancer are discussed.

ETV1 directs androgen metabolism and confers aggressive prostate cancer in targeted mice and patients

Distinguishing aggressive from indolent disease and developing effective therapy for advanced disease are the major challenges in prostate cancer research. Chromosomal rearrangements involving ETS transcription factors, such as ERG and ETV1, occur frequently in prostate cancer. How they contribute to tumorigenesis and whether they play similar or distinct in vivo roles remain elusive. Here we show that in mice with ERG or ETV1 targeted to the endogenous Tmprss2 locus, either factor cooperated with loss of a single copy of Pten, leading to localized cancer, but only ETV1 appeared to support development of invasive adenocarcinoma under the background of full Pten loss. Mechanistic studies demonstrated that ERG and ETV1 control a common transcriptional network but largely in an opposing fashion. In particular, while ERG negatively regulates the androgen receptor (AR) transcriptional program, ETV1 cooperates with AR signaling by favoring activation of the AR transcriptional program. Furthermore, we found that ETV1 expression, but not that of ERG, promotes autonomous testosterone production. Last, we confirmed the association of an ETV1 expression signature with aggressive disease and poorer outcome in patient data. The distinct biology of ETV1-associated prostate cancer suggests that this disease class may require new therapies directed to underlying programs controlled by ETV1.

Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors

Background

Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors.

Results

As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions.

Conclusions

Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.

Chromatin immunoprecipitation assay as a tool for analyzing transcription factor activity

Differential gene expression is facilitated by transcriptional regulatory mechanisms and chromatin modifications through DNA-protein interactions. One of the widely used assays to study this is chromatin immunoprecipitation (ChIP) assay, which enables analysis of association of regulatory molecules to specific promoters and histone modifications in vivo. This is of immense value as ChIP assays can provide glimpse of the regulatory mechanisms involved in gene expression in vivo. This article outlines the general strategies and protocols to study ChIP assays in differential recruitment of transcriptional factors (TFs) and also global analysis of transcription factor recruitment is discussed. Further, the applications of ChIP assays for discovering novel genes that are dependent on specific transcription factors were addressed.

A Myc network accounts for similarities between embryonic stem and cancer cell transcription programs

c-Myc (Myc) is an important transcriptional regulator in embryonic stem (ES) cells, somatic cell reprogramming, and cancer. Here, we identify a Myc-centered regulatory network in ES cells by combining protein-protein and protein-DNA interaction studies and show that Myc interacts with the NuA4 complex, a regulator of ES cell identity. In combination with regulatory network information, we define three ES cell modules (Core, Polycomb, and Myc) and show that the modules are functionally separable, illustrating that the overall ES cell transcription program is composed of distinct units. With these modules as an analytical tool, we have reassessed the hypothesis linking an ES cell signature with cancer or cancer stem cells. We find that the Myc module, independent of the Core module, is active in various cancers and predicts cancer outcome. The apparent similarity of cancer and ES cell signatures reflects, in large part, the pervasive nature of Myc regulatory networks.

TIF1gamma controls erythroid cell fate by regulating transcription elongation

Recent genome-wide studies have demonstrated that pausing of RNA polymerase II (Pol II) occurred on many vertebrate genes. By genetic studies in the zebrafish tif1gamma mutant moonshine we found that loss of function of Pol II-associated factors PAF or DSIF rescued erythroid gene transcription in tif1gamma-deficient animals. Biochemical analysis established physical interactions among TIF1gamma, the blood-specific SCL transcription complex, and the positive elongation factors p-TEFb and FACT. Chromatin immunoprecipitation assays in human CD34(+) cells supported a TIF1gamma-dependent recruitment of positive elongation factors to erythroid genes to promote transcription elongation by counteracting Pol II pausing. Our study establishes a mechanism for regulating tissue cell fate and differentiation through transcription elongation.

Circadian transcription in liver

Circadian rhythms regulate a wide range of cellular, physiological, metabolic and behavioral activities in mammals. The complexity of tissue- and day-time specific regulation of thousands of clock controlled genes (CCGs) suggests that many transcriptional regulators are involved. Our bioinformatic analysis is based on two published DNA-array studies from mouse liver. We search overrepresented transcription factor binding sites in promoter regions of CCGs using GC-matched controls. Analyzing a large set of CCG promoters, we find known motifs such as E-boxes, D-boxes and cAMP responsive elements. In addition, we find overrepresented GC-rich motifs (Sp1, ETF, Nrf1), AT-rich motifs (TBP, Fox04, MEF-2), Y-box motifs (NF-Y, C/EBP) and cell cycle regulators (E2F, Elk-1). In a subset of system-driven genes, we find overrepresented motifs of the serum response factor SRF and the estrogen receptor ER. The analysis of published ChIP data reveals that some of our predicted regulators (C/EBP, E2F, HNF-1, Myc, MEF-2) target relatively many clock controlled genes. Our analysis of CCG promoters contributes to an understanding of the complex transcriptional regulation of circadian rhythms in liver.

[Genomic analysis of DNA-protein interaction by chromatin immunoprecipitation]

Chromatin immunoprecipitation (ChIP) is an effective technique to analyze the interactions of DNA binding proteins with the genome in vivo. ChIP coupled with high density microarray (ChIP-chip) or high-throughput sequencing (ChIP-Seq) has generated large amount of data and expected to allow the development of a network describing the cellular transcriptional regulation. Here, we reviewed the ChIP, ChIP-chip, and ChIP-Seq techniques as well as their perspectives. Focus is given to data analysis of ChIP-Seq and the applications of ChIP-chip and ChIP-Seq.

Probabilistic peak calling and controlling false discovery rate estimations in transcription factor binding site mapping from ChIP-seq

Localizing the binding sites of regulatory proteins is becoming increasingly feasible and accurate. This is due to dramatic progress not only in chromatin immunoprecipitation combined by next-generation sequencing (ChIP-seq) but also in advanced statistical analyses. A fundamental issue, however, is the alarming number of false positive predictions. This problem can be remedied by improved peak calling methods of twin peaks, one at each strand of the DNA, kernel density estimators, and false discovery rate estimations based on control libraries. Predictions are filtered by de novo motif discovery in the peak environments. These methods have been implemented in, among others, Valouev et al.'s Quantitative Enrichment of Sequence Tags (QuEST) software tool. We demonstrate the prediction of the human growth-associated binding protein (GABPalpha) based on ChIP-seq observations.

A functional analysis of the CREB signaling pathway using HaloCHIP-chip and high throughput reporter assays

Background

Regulation of gene expression is essential for normal development and cellular growth. Transcriptional events are tightly controlled both spatially and temporally by specific DNA-protein interactions. In this study we finely map the genome-wide targets of the CREB protein across all known and predicted human promoters, and characterize the functional consequences of a subset of these binding events using high-throughput reporter assays. To measure CREB binding, we used HaloCHIP, an antibody-free alternative to the ChIP method that utilizes the HaloTag fusion protein, and also high-throughput promoter-luciferase reporter assays, which provide rapid and quantitative screening of promoters for transcriptional activation or repression in living cells.

Results

In analysis of CREB genome-wide binding events using a comprehensive DNA microarray of human promoters, we observe for the first time that CREB has a strong preference for binding at bidirectional promoters and unlike unidirectional promoters, these binding events often occur downstream of transcription start sites. Comparison between HaloCHIP-chip and ChIP-chip data reveal this to be true for both methodologies, indicating it is not a bias of the technology chosen. Transcriptional data obtained from promoter-luciferase reporter arrays also show an unprecedented, high level of activation of CREB-bound promoters in the presence of the co-activator protein TORC1.

Conclusion

These data suggest for the first time that TORC1 provides directional information when CREB is bound at bidirectional promoters and possible pausing of the CREB protein after initial transcriptional activation. Also, this combined approach demonstrates the ability to more broadly characterize CREB protein-DNA interactions wherein not only DNA binding sites are discovered, but also the potential of the promoter sequence to respond to CREB is evaluated.

Serial analysis of binding elements for transcription factors

The ability to determine genome-wide location of transcription factor binding sites (TFBS) is crucial for elucidating gene regulatory networks in human cells during normal development and disease such as tumorigenesis. To achieve this goal, we developed a method called serial analysis of binding elements for transcription factors (SABE) for globally identifying TFBS in human or other mammalian genomes. In this method, a specific antibody targeting a DNA-binding transcription factor of interest is used to pull down the transcription factor and its bound DNA elements through chromatin immunoprecipitation (ChIP). ChIP DNA fragments are further enriched by subtractive hybridization against non-enriched DNA and analyzed through generation of sequence tags similar to serial analysis of gene expression (SAGE). The SABE method circumvents the need for microarrays and is able to identify immunoprecipitated loci in an unbiased manner. The combination of ChIP, subtractive hybridization, and SAGE-type methods is advantageous over other similar strategies to reduce the level of intrinsic noise sequences that is typically present in ChIP samples from human or other mammalian cells.

Emerging methods for analysis of the cancer methylome

CpG island hypermethylation plays a key role in the silencing of cancer-related genes. In recent years, some new and effective methods have been developed for high-throughput analysis of DNA methylation, and have provided DNA methylation markers as powerful tools for the development of innovative diagnostic and therapeutic strategies in cancer. In this review, we describe various current and emerging technologies for studying the DNA methylation profile in cancer including: the isoschizomers and gel-based methylation analysis, the microarray-based methylation analysis and the sequencing-based methylation analysis. All of these techniques have advantages and disadvantages, such as sensitivity, specificity, analysis scale and cost. In the coming years, newer platforms of low cost, high-throughput and greater expediency, and speed for cancer methylome analysis will be developed.

PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

Chromatin immunoprecipitation (ChIP) followed by tag sequencing (ChIP-seq) using high-throughput next-generation instrumentation is fast, replacing chromatin immunoprecipitation followed by genome tiling array analysis (ChIP-chip) as the preferred approach for mapping of sites of transcription-factor binding and chromatin modification. Using two deeply sequenced data sets for human RNA polymerase II and STAT1, each with matching input-DNA controls, we describe a general scoring approach to address unique challenges in ChIP-seq data analysis. Our approach is based on the observation that sites of potential binding are strongly correlated with signal peaks in the control, likely revealing features of open chromatin. We develop a two-pass strategy called PeakSeq to compensate for this. A two-pass strategy compensates for signal caused by open chromatin, as revealed by inclusion of the controls. The first pass identifies putative binding sites and compensates for genomic variation in the 'mappability' of sequences. The second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. Our scoring procedure enables us to optimize experimental design by estimating the depth of sequencing required for a desired level of coverage and demonstrating that more than two replicates provides only a marginal gain in information.

Prediction and integration of regulatory and protein-protein interactions

Knowledge of transcriptional regulatory interactions (TRIs) is essential for exploring functional genomics and systems biology in any organism. While several results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast ( 1 ) and worm ( 2 ). Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a method for the integration of various data sets to predict TRIs for 54 organisms in the Bioverse ( 3 ). We describe how to compile and handle various formats and identifiers of data sets from different sources and how to predict TRIs using a homology-based approach, utilizing the compiled data sets. Integrated data sets include experimentally verified TRIs, binding sites of transcription factors, promoter sequences, protein subcellular localization, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein-protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological dynamics of an integrated network with gene expression under different conditions, essential for exploring functional genomics and systems biology.

Novel insights into adipogenesis from omics data

Obesity, the excess accumulation of adipose tissue, is one of the most pressing health problems in both the Western world and in developing countries. Adipose tissue growth results from two processes: the increase in number of adipocytes (hyperplasia) that develop from precursor cells, and the growth of individual fat cells (hypertrophy) due to incorporation of triglycerides. Adipogenesis, the process of fat cell development, has been extensively studied using various cell and animal models. While these studies pointed out a number of key factors involved in adipogenesis, the list of molecular components is far from complete. The advance of high-throughput technologies has sparked many experimental studies aimed at the identification of novel molecular components regulating adipogenesis. This paper examines the results of recent studies on adipogenesis using high-throughput technologies. Specifically, it provides an overview of studies employing microarrays for gene expression profiling and studies using gel based and non-gel based proteomics as well as a chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) or sequencing (ChIP-seq). Due to the maturity of the technology, the bulk of the available data was generated using microarrays. Therefore these data sets were not only reviewed but also underwent meta analysis. The review also shows that large-scale omics technologies in conjunction with sophisticated bioinformatics analyses can provide not only a list of novel players, but also a global view on biological processes and molecular networks. Finally, developing technologies and computational challenges associated with the data analyses are highlighted, and an outlook on the questions not previously addressed is provided.

ChromaSig: a probabilistic approach to finding common chromatin signatures in the human genome

Computational methods to identify functional genomic elements using genetic information have been very successful in determining gene structure and in identifying a handful of cis-regulatory elements. But the vast majority of regulatory elements have yet to be discovered, and it has become increasingly apparent that their discovery will not come from using genetic information alone. Recently, high-throughput technologies have enabled the creation of information-rich epigenetic maps, most notably for histone modifications. However, tools that search for functional elements using this epigenetic information have been lacking. Here, we describe an unsupervised learning method called ChromaSig to find, in an unbiased fashion, commonly occurring chromatin signatures in both tiling microarray and sequencing data. Applying this algorithm to nine chromatin marks across a 1% sampling of the human genome in HeLa cells, we recover eight clusters of distinct chromatin signatures, five of which correspond to known patterns associated with transcriptional promoters and enhancers. Interestingly, we observe that the distinct chromatin signatures found at enhancers mark distinct functional classes of enhancers in terms of transcription factor and coactivator binding. In addition, we identify three clusters of novel chromatin signatures that contain evolutionarily conserved sequences and potential cis-regulatory elements. Applying ChromaSig to a panel of 21 chromatin marks mapped genomewide by ChIP-Seq reveals 16 classes of genomic elements marked by distinct chromatin signatures. Interestingly, four classes containing enrichment for repressive histone modifications appear to be locally heterochromatic sites and are enriched in quickly evolving regions of the genome. The utility of this approach in uncovering novel, functionally significant genomic elements will aid future efforts of genome annotation via chromatin modifications.

The DNA in eukaryotes is packaged by histones. Interestingly, histones can be marked by a variety of posttranslational modifications, and it has been hypothesized that distinct combinations of histone modifications mark at distinct functional regions of the genome. The study of histone modifications has been aided by the development of high-throughput techniques to map a wide assortment of histone modifications on a global scale. However, because much of our current understanding of the human genome is concentrated on promoters, most studies have only examined histone modifications at these well-defined sites, ignoring the vast majority of the genome. To aid in the discovery of functional elements outside of these well-annotated loci, we develop an unbiased method that searches for commonly occurring histone modification patterns on a global scale without using any annotation information. This method recovers known patterns associated with transcriptional enhancers and promoters. Supporting the histone code hypothesis, we discover that the different functional activities of enhancers are closely associated with the presence of different histone modification patterns. We also discover several novel patterns that likely contain other potential regulatory elements. As the availability of large-scale histone modification data increases, the ability of methods such as the one presented here to concisely describe commonly occurring chromatin signatures, thereby abstracting away irrelevant or redundant data, will become increasingly more critical.

A high-throughput percentage-of-binding strategy to measure binding energies in DNA-protein interactions: application to genome-scale site discovery

Quantifying the binding energy in DNA–protein interactions is of critical importance to understand transcriptional regulation. Based on a simple computational model, this study describes a high-throughput percentage-of-binding strategy to measure the binding energy in DNA–protein interactions between the Shewanella oneidensis ArcA two-component transcription factor protein and a systematic set of mutants in an ArcA-P (phosphorylated ArcA) binding site. The binding energies corresponding to each of the 4 nt at each position in the 15-bp binding site were used to construct a position-specific energy matrix (PEM) that allowed a reliable prediction of ArcA-P binding sites not only in Shewanella but also in related bacterial genomes.

Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: current and developing technologies

The most common form of genetic variation, single nucleotide polymorphisms or SNPs, can affect the way an individual responds to the environment and modify disease risk. Although most of the millions of SNPs have little or no effect on gene regulation and protein activity, there are many circumstances where base changes can have deleterious effects. Non-synonymous SNPs that result in amino acid changes in proteins have been studied because of their obvious impact on protein activity. It is well known that SNPs within regulatory regions of the genome can result in disregulation of gene transcription. However, the impact of SNPs located in putative regulatory regions, or rSNPs, is harder to predict for two primary reasons. First, the mechanistic roles of non-coding genomic sequence remain poorly defined. Second, experimental validation of the functional consequences of rSNPs is often slow and laborious. In this review, we summarize traditional and novel methodologies for candidate rSNPs selection, in particular in silico techniques that aid in candidate rSNP selection. Additionally we will discuss molecular biological techniques that assess the impact of rSNPs on binding of regulatory machinery, as well as functional consequences on transcription. Standard techniques such as EMSA and luciferase reporter constructs are still widely used to assess effects of rSNPs on binding and gene transcription; however, these protocols are often bottlenecks in the discovery process. Therefore, we highlight novel and developing high-throughput protocols that promise to aid in shortening the process of rSNP validation. Given the large amount of genomic information generated from a multitude of re-sequencing and genome-wide SNP array efforts, future focus should be to develop validation techniques that will allow greater understanding of the impact these polymorphisms have on human health and disease.

In silico regulatory analysis for exploring human disease progression

Background

An important goal in bioinformatics is to unravel the network of transcription factors (TFs) and their targets. This is important in the human genome, where many TFs are involved in disease progression. Here, classification methods are applied to identify new targets for 152 transcriptional regulators using publicly-available targets as training examples. Three types of sequence information are used: composition, conservation, and overrepresentation.

Results

Starting with 8817 TF-target interactions we predict an additional 9333 targets for 152 TFs. Randomized classifiers make few predictions (~2/18660) indicating that our predictions for many TFs are significantly enriched for true targets. An enrichment score is calculated and used to filter new predictions.

Two case-studies for the TFs OCT4 and WT1 illustrate the usefulness of our predictions:

• Many predicted OCT4 targets fall into the Wnt-pathway. This is consistent with known biology as OCT4 is developmentally related and Wnt pathway plays a role in early development.

• Beginning with 15 known targets, 354 predictions are made for WT1. WT1 has a role in formation of Wilms' tumor. Chromosomal regions previously implicated in Wilms' tumor by cytological evidence are statistically enriched in predicted WT1 targets. These findings may shed light on Wilms' tumor progression, suggesting that the tumor progresses either by loss of WT1 or by loss of regions harbouring its targets.

• Targets of WT1 are statistically enriched for cancer related functions including metastasis and apoptosis. Among new targets are BAX and PDE4B, which may help mediate the established anti-apoptotic effects of WT1.

• Of the thirteen TFs found which co-regulate genes with WT1 (p ≤ 0.02), 8 have been previously implicated in cancer. The regulatory-network for WT1 targets in genomic regions relevant to Wilms' tumor is provided.

Conclusion

We have assembled a set of features for the targets of human TFs and used them to develop classifiers for the determination of new regulatory targets. Many predicted targets are consistent with the known biology of their regulators, and new targets for the Wilms' tumor regulator, WT1, are proposed. We speculate that Wilms' tumor development is mediated by chromosomal rearrangements in the location of WT1 targets.

Reviewers

This article was reviewed by Trey Ideker, Vladimir A. Kuznetsov(nominated by Frank Eisenhaber), and Tzachi Pilpel.

Genome-wide analysis of protein-DNA interactions

The human genome is predominantly composed of nonprotein-coding sequences whose function remains largely undefined. A significant portion of the noncoding DNA is believed to serve as transcriptional regulatory elements that control gene expression in specific cell types at appropriate developmental stages. Identifying these regulatory sequences and determining the mechanisms by which they act present a great challenge in the postgenomic era. Previous investigations using genetic, molecular, and biochemical approaches have uncovered a large number of proteins involved in regulating transcription. Knowledge of the genomic locations of DNA binding for these proteins in the nucleus should define the identity and nature of the transcriptional regulatory sequences and reveal the gene regulatory networks in cells. Chromatin immunoprecipitation (ChIP) is a common method for detecting interactions between a protein and a DNA sequence in vivo. In recent years, this method has been combined with DNA microarrays and other high-throughput technologies to enable genome-wide identification of DNA-binding sites for various nuclear proteins. Here, we review recent advances in ChIP-based methods for genome-wide detection of protein-DNA interactions, and discuss their significance in enhancing our knowledge of the gene regulatory networks and epigenetic mechanisms in cells.

An extended transcriptional network for pluripotency of embryonic stem cells

Much attention has focused on a small set of transcription factors that maintain human or mouse embryonic stem (ES) cells in a pluripotent state. To gain a more complete understanding of the regulatory network that maintains this state, we identified target promoters of nine transcription factors, including somatic cell reprogramming factors (Oct4, Sox2, Klf4, and c-Myc) and others (Nanog, Dax1, Rex1, Zpf281, and Nac1), on a global scale in mouse ES cells. We found that target genes fall into two classes: promoters bound by few factors tend to be inactive or repressed, whereas promoters bound by more than four factors are largely active in the pluripotent state and become repressed upon differentiation. Furthermore, we propose a transcriptional hierarchy for reprogramming factors and broadly distinguish targets of c-Myc versus other factors. Our data provide a resource for exploration of the complex network maintaining pluripotency.

Velvet: algorithms for de novo short read assembly using de Bruijn graphs

We have developed a new set of algorithms, collectively called "Velvet," to manipulate de Bruijn graphs for genomic sequence assembly. A de Bruijn graph is a compact representation based on short words (k-mers) that is ideal for high coverage, very short read (25-50 bp) data sets. Applying Velvet to very short reads and paired-ends information only, one can produce contigs of significant length, up to 50-kb N50 length in simulations of prokaryotic data and 3-kb N50 on simulated mammalian BACs. When applied to real Solexa data sets without read pairs, Velvet generated contigs of approximately 8 kb in a prokaryote and 2 kb in a mammalian BAC, in close agreement with our simulated results without read-pair information. Velvet represents a new approach to assembly that can leverage very short reads in combination with read pairs to produce useful assemblies.

Global identification of Myc target genes reveals its direct role in mitochondrial biogenesis and its E-box usage in vivo

The Myc oncoprotein is a transcription factor involved in a variety of human cancers. Overexpression of Myc is associated with malignant transformation. In normal cells, Myc is induced by mitotic signals, and in turn, it regulates the expression of downstream target genes. Although diverse roles of Myc have been predicted from many previous studies, detailed functions of Myc targets are still unclear. By combining chromatin immunoprecipitation (ChIP) and promoter microarrays, we identified a total of 1469 Myc direct target genes, the majority of which are novel, in HeLa cells and human primary fibroblasts. We observed dramatic changes of Myc occupancy at its target promoters in foreskin fibroblasts in response to serum stimulation. Among the targets of Myc, 107 were nuclear encoded genes involved in mitochondrial biogenesis. Genes with important roles in mitochondrial replication and biogenesis, such as POLG, POLG2, and NRF1 were identified as direct targets of Myc, confirming a direct role for Myc in regulating mitochondrial biogenesis. Analysis of target promoter sequences revealed a strong preference for Myc occupancy at promoters containing one of several described consensus sequences, CACGTG, in vivo. This study thus sheds light on the transcriptional regulatory networks mediated by Myc in vivo.

Paired-end diTagging for transcriptome and genome analysis

The Paired-End diTagging (PET) procedure enables one to obtain sequence information from both termini of any contiguous DNA fragment. This is achieved by a series of enzymatic manipulations that introduce MmeI sites directly flanking each DNA insert during the construction of a plasmid library. Subsequent MmeI digestion and self-ligation results in the production of covalently-linked paired-end ditags (PETs) that can be extracted and then concatenated for efficient sequencing. By mapping the PET sequences to assembled genomes, the original DNA fragments from which the PETs were derived can be precisely localized. This unit details two applications of PET technology. In GIS-PET, ditagging of mRNA converted to full-length cDNA enables whole-transcriptome analysis, including novel gene identification, gene prediction validation, and gene expression studies. In ChIP-PET, ditagging of chromatin immunoprecipitation-enriched genomic DNA fragments enables the global mapping of transcription factor binding sites. A recent innovation (Multiplex Sequencing of Paired-End ditags; MS-PET) enables PETs to be sequenced using high-throughput 454 sequencing, greatly increasing the amount of data that can be collected in each run.

Modeling transcriptome based on transcript-sampling data

Background

Newly-evolved multiplex sequencing technology has been bringing transcriptome sequencing into an unprecedented depth. Millions of transcript tags now can be acquired in a single experiment through parallelization. The significant increase in throughput and reduction in cost required us to address some fundamental questions, such as how many transcript tags do we have to sequence for a given transcriptome? How could we estimate the total number of unique transcripts for different cell types (transcriptome diversity) and the distribution of their copy numbers (transcriptome dynamics)? What is the probability that a transcript with a given expression level to be detected at a certain sampling depth?

Methodology/Principal Findings

We developed a statistical model to evaluate these parameters based on transcriptome-sampling data. Three mixture models were exploited for their potentials to model the sampling frequencies. We demonstrated that relative abundances of all transcripts in a transcriptome follow the generalized inverse Gaussian distribution. The widely known beta and gamma distributions failed to fulfill the singular characteristics of relative abundance distribution, i.e., highly skewed toward zero and with a long tail. An estimator of transcriptome diversity and an analytical form of sampling growth curve were proposed in a coherent framework. Experimental data fitted this model very well and Monte Carlo simulations based on this model replicated sampling experiments in a remarkable precision.

Conclusions

Taking human embryonic stem cell as a prototype, we demonstrated that sequencing tens of thousands of transcript tags in an ordinary EST/SAGE experiment was far from sufficient. In order to fully characterize a human transcriptome, millions of transcript tags had to be sequenced. This model lays a statistical basis for transcriptome-sampling experiments and in essence can be used in all sampling-based data.

Identifying chromosomal targets of DNA-binding proteins by Sequence Tag Analysis of Genomic Enrichment (STAGE)

Sequence Tag Analysis of Genomic Enrichment (STAGE) is a method for experimentally identifying the in vivo chromosomal targets of DNA-binding proteins in any sequenced genome. STAGE generates 21-bp tags derived from DNA isolated by chromatin immunoprecipitation (ChIP; UNIT 21.3). Concatamers of tags are cloned and sequenced to yield a STAGE library. Tags in the library represent DNA fragments that were occupied by the DNA-binding protein, and mapping these tag sequences to the genome identifies the binding loci of the DNA-binding protein in vivo. STAGE can be applied to any sequenced genome to identify targets of DNA-binding proteins without requiring extensive microarray resources.

Central corticosteroid actions: Search for gene targets

Although many of the physiological effects of corticosteroid stress hormones on neuronal function are well recognised, the underlying genomic mechanisms are only starting to be elucidated. Linking physiology and genomics has proven to be a complicated task, despite the emergence of large-scale gene expression profiling technology in the last decade. This is in part due to the complexity of glucocorticoid-signaling, in part due to the complexity of the brain itself. The presence of a binary receptor system for glucocorticoid hormones in limbic brain structures, the coexistence of membrane and intracellular receptors and the highly contextual action of glucocorticoids contribute to this complexity. In addition, the anatomical complexity, extensive cellular heterogeneity of brain and the modest changes in gene expression (mostly in the range of 10-30%) hamper detection of responsive genes, in particular of low abundant transcripts, such as many neurotransmitter receptors and growth factors. Nonetheless, ongoing research into central targets of glucocorticoids has identified many different functional gene classes that underlie the diverse effects of glucocorticoids on brain function. These functional classes include genes involved in energy metabolism, signal transduction, neuronal structure, vesicle dynamics, neurotransmitter catabolism, cell adhesion, genes encoding neurotrophic factors and their receptors and genes involved in regulating glucocorticoid-signalling. The aim of this review is to give an overview of the current status of the field on identification of central corticosteroid targets, discuss the opportunities and pitfalls and highlight new developments in understanding central corticosteroid action.

Identification of transcription factor target genes by ChIP display

Transcription factors play pivotal roles in the control of cell growth and differentiation in health and disease. In the post-genomic era, it has become possible to locate the regions occupied by transcription factors throughout the genome, leading to better understanding of their mechanism of action and the genes that they regulate. All methods for transcription factor location analysis utilize chromatin immunoprecipitation (ChIP). Although ChIP was initially used to test whether a protein binds to a candidate promoter in living cells, newly developed methods allow the unbiased identification of novel targets of transcription factors. This chapter describes ChIP Display, an affordable method for transcription factor location analysis. Despite being relatively low throughput compared with alternative methods such as ChIP-chip and ChIP-SAGE, ChIP Display provides even small molecular biology laboratories with the opportunity to discover novel targets of any transcription factor, for which high-quality antibodies are available.

Predicting combinatorial binding of transcription factors to regulatory elements in the human genome by association rule mining

Background

Cis-acting transcriptional regulatory elements in mammalian genomes typically contain specific combinations of binding sites for various transcription factors. Although some cis-regulatory elements have been well studied, the combinations of transcription factors that regulate normal expression levels for the vast majority of the 20,000 genes in the human genome are unknown. We hypothesized that it should be possible to discover transcription factor combinations that regulate gene expression in concert by identifying over-represented combinations of sequence motifs that occur together in the genome. In order to detect combinations of transcription factor binding motifs, we developed a data mining approach based on the use of association rules, which are typically used in market basket analysis. We scored each segment of the genome for the presence or absence of each of 83 transcription factor binding motifs, then used association rule mining algorithms to mine this dataset, thus identifying frequently occurring pairs of distinct motifs within a segment.

Results

Support for most pairs of transcription factor binding motifs was highly correlated across different chromosomes although pair significance varied. Known true positive motif pairs showed higher association rule support, confidence, and significance than background. Our subsets of high-confidence, high-significance mined pairs of transcription factors showed enrichment for co-citation in PubMed abstracts relative to all pairs, and the predicted associations were often readily verifiable in the literature.

Conclusion

Functional elements in the genome where transcription factors bind to regulate expression in a combinatorial manner are more likely to be predicted by identifying statistically and biologically significant combinations of transcription factor binding motifs than by simply scanning the genome for the occurrence of binding sites for a single transcription factor.

Serial analysis of gene expression

Serial analysis of gene expression (SAGE) is a method used to obtain comprehensive, unbiased and quantitative gene-expression profiles. Its major advantage over arrays is that it does not require a priori knowledge of the genes to be analyzed and reflects absolute mRNA levels. Since the original SAGE protocol was developed in a short-tag (10-bp) format, several modifications have been made to produce longer SAGE tags for more precise gene identification and to decrease the amount of starting material necessary. Several SAGE-like methods have also been developed for the genome-wide analysis of DNA copy-number changes and methylation patterns, chromatin structure and transcription factor targets. In this protocol, we describe the 17-bp longSAGE method for transcriptome profiling optimized for a small amount of starting material. The generation of such libraries can be completed in 7-10 d, whereas sequencing and data analysis require an additional 2-3 wk.

Epigenetic regulation of hematopoietic differentiation by Gfi-1 and Gfi-1b is mediated by the cofactors CoREST and LSD1

Gfi-1 and Gfi-1b are homologous transcriptional repressors involved in diverse developmental contexts, including hematopoiesis and oncogenesis. Transcriptional repression by Gfi proteins requires the conserved SNAG domain. To elucidate the function of Gfi proteins, we purified Gfi-1b complexes and identified interacting proteins. Prominent among these is the corepressor CoREST, the histone demethylase LSD1, and HDACs 1 and 2. CoREST and LSD1 associate with Gfi-1/1b via the SNAG repression domain. Gfi-1b further recruits these cofactors to the majority of target gene promoters in vivo. Inhibition of CoREST and LSD1 perturbs differentiation of erythroid, megakaryocytic, and granulocytic cells as well as primary erythroid progenitors. LSD1 depletion derepresses Gfi targets in lineage-specific patterns, accompanied by enhanced histone 3 lysine 4 methylation at the respective promoters. Overall, we show that chromatin regulatory proteins CoREST and LSD1 mediate transcriptional repression by Gfi proteins. Lineage-restricted deployment of these cofactors through interaction with Gfi proteins controls hematopoietic differentiation.

Mapping of transcription factor binding regions in mammalian cells by ChIP: comparison of array- and sequencing-based technologies

Recent progress in mapping transcription factor (TF) binding regions can largely be credited to chromatin immunoprecipitation (ChIP) technologies. We compared strategies for mapping TF binding regions in mammalian cells using two different ChIP schemes: ChIP with DNA microarray analysis (ChIP-chip) and ChIP with DNA sequencing (ChIP-PET). We first investigated parameters central to obtaining robust ChIP-chip data sets by analyzing STAT1 targets in the ENCODE regions of the human genome, and then compared ChIP-chip to ChIP-PET. We devised methods for scoring and comparing results among various tiling arrays and examined parameters such as DNA microarray format, oligonucleotide length, hybridization conditions, and the use of competitor Cot-1 DNA. The best performance was achieved with high-density oligonucleotide arrays, oligonucleotides >/=50 bases (b), the presence of competitor Cot-1 DNA and hybridizations conducted in microfluidics stations. When target identification was evaluated as a function of array number, 80%-86% of targets were identified with three or more arrays. Comparison of ChIP-chip with ChIP-PET revealed strong agreement for the highest ranked targets with less overlap for the low ranked targets. With advantages and disadvantages unique to each approach, we found that ChIP-chip and ChIP-PET are frequently complementary in their relative abilities to detect STAT1 targets for the lower ranked targets; each method detected validated targets that were missed by the other method. The most comprehensive list of STAT1 binding regions is obtained by merging results from ChIP-chip and ChIP-sequencing. Overall, this study provides information for robust identification, scoring, and validation of TF targets using ChIP-based technologies.

Finding cis-regulatory elements using comparative genomics: some lessons from ENCODE data

Identification of functional genomic regions using interspecies comparison will be most effective when the full span of relationships between genomic function and evolutionary constraint are utilized. We find that sets of putative transcriptional regulatory sequences, defined by ENCODE experimental data, have a wide span of evolutionary histories, ranging from stringent constraint shown by deep phylogenetic comparisons to recent selection on lineage-specific elements. This diversity of evolutionary histories can be captured, at least in part, by the suite of available comparative genomics tools, especially after correction for regional differences in the neutral substitution rate. Putative transcriptional regulatory regions show alignability in different clades, and the genes associated with them are enriched for distinct functions. Some of the putative regulatory regions show evidence for recent selection, including a primate-specific, distal promoter that may play a novel role in regulation.

Mapping the chromosomal targets of STAT1 by Sequence Tag Analysis of Genomic Enrichment (STAGE)

Identifying the genome-wide binding sites of transcription factors is important in deciphering transcriptional regulatory networks. ChIP-chip (Chromatin immunoprecipitation combined with microarrays) has been widely used to map transcription factor binding sites in the human genome. However, whole genome ChIP-chip analysis is still technically challenging in vertebrates. We recently developed STAGE as an unbiased method for identifying transcription factor binding sites in the genome. STAGE is conceptually based on SAGE, except that the input is ChIP-enriched DNA. In this study, we implemented an improved sequencing strategy and analysis methods and applied STAGE to map the genomic binding profile of the transcription factor STAT1 after interferon treatment. STAT1 is mainly responsible for mediating the cellular responses to interferons, such as cell proliferation, apoptosis, immune surveillance, and immune responses. We present novel algorithms for STAGE tag analysis to identify enriched loci with high specificity, as verified by quantitative ChIP. STAGE identified several previously unknown STAT1 target genes, many of which are involved in mediating the response to interferon-gamma signaling. STAGE is thus a viable method for identifying the chromosomal targets of transcription factors and generating meaningful biological hypotheses that further our understanding of transcriptional regulatory networks.

A new binding motif for the transcriptional repressor REST uncovers large gene networks devoted to neuronal functions

The repressor element 1 (RE1) silencing transcription factor (REST) helps preserve the identity of nervous tissue by silencing neuronal genes in non-neural tissues. Moreover, in an epithelial model of tumorigenesis, loss of REST function is associated with loss of adhesion, suggesting the aberrant expression of REST-controlled genes encoding this property. To date, no adhesion molecules under REST control have been identified. Here, we used serial analysis of chromatin occupancy to perform genome-wide identification of REST-occupied target sequences (RE1 sites) in a kidney cell line. We discovered novel REST-binding motifs and found that the number of RE1 sites far exceeded previous estimates. A large family of targets encoding adhesion proteins was identified, as were genes encoding signature proteins of neuroendocrine tumors. Unexpectedly, genes considered exclusively non-neuronal also contained an RE1 motif and were expressed in neurons. This supports the model that REST binding is a critical determinant of neuronal phenotype.

The new frontier in cancer research: Deciphering cancer epigenetics

Cancer has long been known to be a disease caused by alterations in the genetic blueprint of cells. In the past decade it has become apparent that epigenetic alterations also underlie the etiology of cancer. Since epigenetic changes may be more facile to reverse than genetic lesions, much research has been invested in their characterization. Success has indeed been booked in the clinic with drugs that erase DNA methylation imprints or that target histone post-translational modifications such as lysine acetylation. However, the actual consequences of current epigenetic pharmacological intervention protocols are still poorly characterized and may be rather pleiotropic in nature. The challenge we face is therefore to define the cellular enzymes responsible for epigenetic modifications at given genes under specific conditions, so as to develop pharmacological agents that target tumorigenic epigenetic lesions while eliciting minimal unwanted side effects. Application of genome-wide analytical tools has begun to provide spatio-temporally resolved data that will be crucial to achieve this goal. Finally, the molecular mode of action of epigenetic drugs may be more intricate than initially thought, involving more than DNA and histones, since it has been reported that transcription (co)factors are themselves also targeted by histone modifying enzymes.

Serial analysis of binding elements for human transcription factors

Serial analysis of binding elements (SABE) is a method that can be used to identify the genome-wide location of transcription factor binding sites in human or other mammalian cells. In this method, a specific antibody targeting a DNA-binding transcription factor of interest is used to pull down the transcription factor and its bound DNA elements through chromatin immunoprecipitation (ChIP). ChIP DNA fragments are further enriched by subtractive hybridization against non-enriched DNA using representational difference analysis (RDA) and analyzed through the generation of sequence tags similar to serial analysis of gene expression (SAGE). The SABE method circumvents the need for microarrays and is able to identify immunoprecipitated loci in an unbiased manner. The combination of ChIP, RDA and SAGE-type methods has advantages over other similar strategies in reducing the level of intrinsic noise sequences that are typically present in ChIP samples from human cells. This protocol takes about 2 weeks to complete.

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

We developed a method, ChIP-sequencing (ChIP-seq), combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing to identify mammalian DNA sequences bound by transcription factors in vivo. We used ChIP-seq to map STAT1 targets in interferon-gamma (IFN-gamma)-stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes. By ChIP-seq, using 15.1 and 12.9 million uniquely mapped sequence reads, and an estimated false discovery rate of less than 0.001, we identified 41,582 and 11,004 putative STAT1-binding regions in stimulated and unstimulated cells, respectively. Of the 34 loci known to contain STAT1 interferon-responsive binding sites, ChIP-seq found 24 (71%). ChIP-seq targets were enriched in sequences similar to known STAT1 binding motifs. Comparisons with two ChIP-PCR data sets suggested that ChIP-seq sensitivity was between 70% and 92% and specificity was at least 95%.

Getting connected: analysis and principles of biological networks

The execution of complex biological processes requires the precise interaction and regulation of thousands of molecules. Systematic approaches to study large numbers of proteins, metabolites, and their modification have revealed complex molecular networks. These biological networks are significantly different from random networks and often exhibit ubiquitous properties in terms of their structure and organization. Analyzing these networks provides novel insights in understanding basic mechanisms controlling normal cellular processes and disease pathologies.

Identification of novel androgen receptor target genes in prostate cancer

BACKGROUND

The androgen receptor (AR) plays critical roles in both androgen-dependent and castrate-resistant prostate cancer (PCa). However, little is known about AR target genes that mediate the receptor's roles in disease progression.

RESULTS

Using Chromatin Immunoprecipitation (ChIP) Display, we discovered 19 novel loci occupied by the AR in castrate resistant C4-2B PCa cells. Only four of the 19 AR-occupied regions were within 10-kb 5'-flanking regulatory sequences. Three were located up to 4-kb 3' of the nearest gene, eight were intragenic and four were in gene deserts. Whereas the AR occupied the same loci in C4-2B (castrate resistant) and LNCaP (androgen-dependent) PCa cells, differences between the two cell lines were observed in the response of nearby genes to androgens. Among the genes strongly stimulated by DHT in C4-2B cells--D-dopachrome tautomerase (DDT), Protein kinase C delta (PRKCD), Glutathione S- transferase theta 2 (GSTT2), Transient receptor potential cation channel subfamily V member 3 (TRPV3), and Pyrroline-5-carboxylate reductase 1 (PYCR1)--most were less strongly or hardly stimulated in LNCaP cells. Another AR target gene, ornithine aminotransferase (OAT), was AR-stimulated in a ligand-independent manner, since it was repressed by AR siRNA knockdown, but not stimulated by DHT. We also present evidence for in vivo AR-mediated regulation of several genes identified by ChIP Display. For example, PRKCD and PYCR1, which may contribute to PCa cell growth and survival, are expressed in PCa biopsies from primary tumors before and after ablation and in metastatic lesions in a manner consistent with AR-mediated stimulation.

CONCLUSION

AR genomic occupancy is similar between LNCaP and C4-2B cells and is not biased towards 5' gene flanking sequences. The AR transcriptionally regulates less than half the genes nearby AR-occupied regions, usually but not always, in a ligand-dependent manner. Most are stimulated and a few are repressed. In general, response is stronger in C4-2B compared to LNCaP cells. Some of the genes near AR-occupied regions appear to be regulated by the AR in vivo as evidenced by their expression levels in prostate cancer tumors of various stages. Several AR target genes discovered in the present study, for example PRKCD and PYCR1, may open avenues in PCa research and aid the development of new approaches for disease management.

Interaction of Guanine, Its Anions, and Radicals with Lysine in Different Charge States

Modification in DNA or protein structure can severely affect DNA-protein interactions and the functioning of biological systems. Some new insights into radiation-induced effects of guanine-lysine interactions have been obtained here by theoretical investigations. Geometries of zwitterionic and non-zwitterionic lysine in different charge states (neutral, radical cation, and protonated cation) were optimized employing the B3LYP/6-31G** and B3LYP/AUG-cc-pVDZ levels of hybrid density functional theory (DFT) and using the second-order Møller-Plesset perturbation theory along with the 6-31G** basis set. In the case of neutral lysine in the gas phase, no zwitterionic structure was obtained. The non-zwitterionic structures of lysine in radical and protonated cationic forms are appreciably more stable than the corresponding zwitterionic structures in the gas phase as obtained at all levels of theory employed here. Binding of guanine and different dehydrogenated guanine radicals with lysine in different charge states was studied at the B3LYP/6-31G** level of DFT. When guanine makes a complex with the lysine radical cation, large amounts of spin and positive charge densities are transferred from the lysine radical cation to guanine and the guanine is thus converted from its normal form to the radical cationic form. Complexation of the lysine radical cation with the H1-hydrogen-abstracted guanine radical leads to CO2 liberation and proton transfer from lysine. These results are compared with the available experimental ones.

Distribution of menin-occupied regions in chromatin specifies a broad role of menin in transcriptional regulation

Menin is the protein product of the MEN1 tumor-suppressor gene; one allele of MEN1 is inactivated in the germ line of patients with "multiple endocrine neoplasia type 1" (MEN1) cancer syndrome. Menin interacts with several proteins involved in transcriptional regulation. RNA expression analyses have identified several menin-regulated genes that could represent proximal or distal interaction sites for menin. This report presents a substantial and unbiased sampling of menin-occupied chromatin regions using Serial Analysis of Chromatin Occupancy; this method combines chromatin immuno-precipitation with Serial Analysis of Gene Expression. Hundreds of menin-occupied genomic sites were identified in promoter regions (32% of menin-occupied loci), near the 3' end of genes (14%), or inside genes (21%), extending other data about menin recruitments to many sites of transcriptional activity. A large number of menin-occupied sites (33%) were located outside known gene regions. Additional annotation of the human genome could help in identifying genes at these loci, or these might be gene-free regions of the genome where menin occupancy could play some structural or regulatory role. Menin occupancy at many intragenic positions distant from the core promoter reveals an unexpected type of menin target region at many loci in the genome. These unbiased data also suggest that menin could play a broad role in transcriptional regulation.