An atlas of combinatorial transcriptional regulation in mouse and man

Genetic variation shapes protein networks mainly through non-transcriptional mechanisms

Networks of co-regulated transcripts in genetically diverse populations have been studied extensively, but little is known about the degree to which these networks cause similar co-variation at the protein level. We quantified 354 proteins in a genetically diverse population of yeast segregants, which allowed for the first time construction of a coherent protein co-variation matrix. We identified tightly co-regulated groups of 36 and 93 proteins that were made up predominantly of genes involved in ribosome biogenesis and amino acid metabolism, respectively. Even though the ribosomal genes were tightly co-regulated at both the protein and transcript levels, genetic regulation of proteins was entirely distinct from that of transcripts, and almost no genes in this network showed a significant correlation between protein and transcript levels. This result calls into question the widely held belief that in yeast, as opposed to higher eukaryotes, ribosomal protein levels are regulated primarily by regulating transcript levels. Furthermore, although genetic regulation of the amino acid network was more similar for proteins and transcripts, regression analysis demonstrated that even here, proteins vary predominantly as a result of non-transcriptional variation. We also found that cis regulation, which is common in the transcriptome, is rare at the level of the proteome. We conclude that most inter-individual variation in levels of these particular high abundance proteins in this genetically diverse population is not caused by variation of their underlying transcripts.

Systematic clustering of transcription start site landscapes

Genome-wide, high-throughput methods for transcription start site (TSS) detection have shown that most promoters have an array of neighboring TSSs where some are used more than others, forming a distribution of initiation propensities. TSS distributions (TSSDs) vary widely between promoters and earlier studies have shown that the TSSDs have biological implications in both regulation and function. However, no systematic study has been made to explore how many types of TSSDs and by extension core promoters exist and to understand which biological features distinguish them. In this study, we developed a new non-parametric dissimilarity measure and clustering approach to explore the similarities and stabilities of clusters of TSSDs. Previous studies have used arbitrary thresholds to arrive at two general classes: broad and sharp. We demonstrated that in addition to the previous broad/sharp dichotomy an additional category of promoters exists. Unlike typical TATA-driven sharp TSSDs where the TSS position can vary a few nucleotides, in this category virtually all TSSs originate from the same genomic position. These promoters lack epigenetic signatures of typical mRNA promoters and a substantial subset of them are mapping upstream of ribosomal protein pseudogenes. We present evidence that these are likely mapping errors, which have confounded earlier analyses, due to the high similarity of ribosomal gene promoters in combination with known G addition bias in the CAGE libraries. Thus, previous two-class separations of promoter based on TSS distributions are motivated, but the ultra-sharp TSS distributions will confound downstream analyses if not removed.

Transcription factor interactions in genomic nuclear receptor function

Transcription factors (TF) regulate gene expression acting as DNA sequence-specific binding factors, orchestrating cofactor recruitment and assembly of the transcriptional machinery. Nuclear receptors, a ligand-inducible TF class, regulate a large proportion of the genome, yet achieve highly cell-specific and context-dependent transcription, despite their widespread expression. High-throughput genome-wide profiling of TF binding reveals a startling proportion of colocalized cell- and context-specific TF-binding patterns, implying TF interactions play a critical role in transcription. These interactions depend on the chromatin architecture, that predominantly acts to predetermine accessibility of TF-binding sites at regulatory elements. Here, we summarize recent findings that highlight the importance of combinatorial TF interactions in determining diverse nuclear receptor-mediated transcriptional responses, emphasizing the significance of chromatin structure in directing TF and nuclear receptor recruitment. Interactions between TFs are likely to be a general mechanism of regulatory factors, contributing to transcriptional control in health and disease.

Cooperative transcription factor associations discovered using regulatory variation

Regulation of gene expression at the transcriptional level is achieved by complex interactions of transcription factors operating at their target genes. Dissecting the specific combination of factors that bind each target is a significant challenge. Here, we describe in detail the Allele Binding Cooperativity test, which uses variation in transcription factor binding among individuals to discover combinations of factors and their targets. We developed the ALPHABIT (a large-scale process to hunt for allele binding interacting transcription factors) pipeline, which includes statistical analysis of binding sites followed by experimental validation, and demonstrate that this method predicts transcription factors that associate with NFκB. Our method successfully identifies factors that have been known to work with NFκB (E2A, STAT1, IRF2), but whose global coassociation and sites of cooperative action were not known. In addition, we identify a unique coassociation (EBF1) that had not been reported previously. We present a general approach for discovering combinatorial models of regulation and advance our understanding of the genetic basis of variation in transcription factor binding.

Enhancers regulate progression of development in mammalian cells

During development and differentiation of an organism, accurate gene regulation is central for cells to maintain and balance their differentiation processes. Transcriptional interactions between cis-acting DNA elements such as promoters and enhancers are the basis for precise and balanced transcriptional regulation. We identified modules of combinations of binding sites in proximal and distal regulatory regions upstream of all transcription start sites (TSSs) in silico and applied these modules to gene expression time-series of mouse embryonic development and differentiation of human stem cells. In addition to tissue-specific regulation controlled by combinations of transcription factors (TFs) binding at promoters, we observed that in particular the combination of TFs binding at promoters together with TFs binding at the respective enhancers regulate highly specifically temporal progression during development: whereas 40% of TFs were specific for time intervals, 79% of TF pairs and even 97% of promoter-enhancer modules showed specificity for single time intervals of the human stem cells. Predominantly SP1 and E2F contributed to temporal specificity at promoters and the forkhead (FOX) family of TFs at enhancer regions. Altogether, we characterized three classes of TFs: with binding sites being enriched at the TSS (like SP1), depleted at the TSS (like FOX), and rather uniformly distributed.

Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function

We collected a massive and heterogeneous dataset of 20 255 gene expression profiles (GEPs) from a variety of human samples and experimental conditions, as well as 8895 GEPs from mouse samples. We developed a mutual information (MI) reverse-engineering approach to quantify the extent to which the mRNA levels of two genes are related to each other across the dataset. The resulting networks consist of 4 817 629 connections among 20 255 transcripts in human and 14 461 095 connections among 45 101 transcripts in mouse, with a inter-species conservation of 12%. The inferred connections were compared against known interactions to assess their biological significance. We experimentally validated a subset of not previously described protein–protein interactions. We discovered co-expressed modules within the networks, consisting of genes strongly connected to each other, which carry out specific biological functions, and tend to be in physical proximity at the chromatin level in the nucleus. We show that the network can be used to predict the biological function and subcellular localization of a protein, and to elucidate the function of a disease gene. We experimentally verified that granulin precursor (GRN) gene, whose mutations cause frontotemporal lobar degeneration, is involved in lysosome function. We have developed an online tool to explore the human and mouse gene networks.

Stage prediction of embryonic stem cell differentiation from genome-wide expression data

MOTIVATION

The developmental stage of a cell can be determined by cellular morphology or various other observable indicators. Such classical markers could be complemented with modern surrogates, like whole-genome transcription profiles, that can encode the state of the entire organism and provide increased quantitative resolution. Recent findings suggest that such profiles provide sufficient information to reliably predict the cell's developmental stage.

RESULTS

We use whole-genome transcription data and several data projection methods to infer differentiation stage prediction models for embryonic cells. Given a transcription profile of an uncharacterized cell, these models can then predict its developmental stage. In a series of experiments comprising 14 datasets from the Gene Expression Omnibus, we demonstrate that the approach is robust and has excellent prediction ability both within a specific cell line and across different cell lines.

AVAILABILITY

Model inference and computational evaluation procedures in the form of Python scripts and accompanying datasets are available at http://www.biolab.si/supp/stagerank.

CONTACT

blaz.zupan@fri.uni-lj.si

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Simplified method to predict mutual interactions of human transcription factors based on their primary structure

Background

Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation.

Methodology

We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems.

Conclusions

The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.

Genome-wide CTCF distribution in vertebrates defines equivalent sites that aid the identification of disease-associated genes

Many genomic alterations associated with human diseases localize in noncoding regulatory elements located far from the promoters they regulate, making it challenging to link noncoding mutations or risk-associated variants with target genes. The range of action of a given set of enhancers is thought to be defined by insulator elements bound by the 11 zinc-finger nuclear factor CCCTC-binding protein (CTCF). Here we analyzed the genomic distribution of CTCF in various human, mouse and chicken cell types, demonstrating the existence of evolutionarily conserved CTCF-bound sites beyond mammals. These sites preferentially flank transcription factor-encoding genes, often associated with human diseases, and function as enhancer blockers in vivo, suggesting that they act as evolutionarily invariant gene boundaries. We then applied this concept to predict and functionally demonstrate that the polymorphic variants associated with multiple sclerosis located within the EVI5 gene impinge on the adjacent gene GFI1.

Inferring transcription factor complexes from ChIP-seq data

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) allows researchers to determine the genome-wide binding locations of individual transcription factors (TFs) at high resolution. This information can be interrogated to study various aspects of TF behaviour, including the mechanisms that control TF binding. Physical interaction between TFs comprises one important aspect of TF binding in eukaryotes, mediating tissue-specific gene expression. We have developed an algorithm, spaced motif analysis (SpaMo), which is able to infer physical interactions between the given TF and TFs bound at neighbouring sites at the DNA interface. The algorithm predicts TF interactions in half of the ChIP-seq data sets we test, with the majority of these predictions supported by direct evidence from the literature or evidence of homodimerization. High resolution motif spacing information obtained by this method can facilitate an improved understanding of individual TF complex structures. SpaMo can assist researchers in extracting maximum information relating to binding mechanisms from their TF ChIP-seq data. SpaMo is available for download and interactive use as part of the MEME Suite (http://meme.nbcr.net).

Measuring the evolutionary rate of protein-protein interaction

Despite our extensive knowledge about the rate of protein sequence evolution for thousands of genes in hundreds of species, the corresponding rate of protein function evolution is virtually unknown, especially at the genomic scale. This lack of knowledge is primarily because of the huge diversity in protein function and the consequent difficulty in gauging and comparing rates of protein function evolution. Nevertheless, most proteins function through interacting with other proteins, and protein-protein interaction (PPI) can be tested by standard assays. Thus, the rate of protein function evolution may be measured by the rate of PPI evolution. Here, we experimentally examine 87 potential interactions between Kluyveromyces waltii proteins, whose one to one orthologs in the related budding yeast Saccharomyces cerevisiae have been reported to interact. Combining our results with available data from other eukaryotes, we estimate that the evolutionary rate of protein interaction is (2.6 ± 1.6) × 10(-10) per PPI per year, which is three orders of magnitude lower than the rate of protein sequence evolution measured by the number of amino acid substitutions per protein per year. The extremely slow evolution of protein molecular function may account for the remarkable conservation of life at molecular and cellular levels and allow for studying the mechanistic basis of human disease in much simpler organisms.

Integrative gene network construction for predicting a set of complementary prostate cancer genes

MOTIVATION

Diagnosis and prognosis of cancer and understanding oncogenesis within the context of biological pathways is one of the most important research areas in bioinformatics. Recently, there have been several attempts to integrate interactome and transcriptome data to identify subnetworks that provide limited interpretations of known and candidate cancer genes, as well as increase classification accuracy. However, these studies provide little information about the detailed roles of identified cancer genes.

RESULTS

To provide more information to the network, we constructed the network by incorporating genetic interactions and manually curated gene regulations to the protein interaction network. To make our newly constructed network cancer specific, we identified edges where two genes show different expression patterns between cancer and normal phenotypes. We showed that the integration of various datasets increased classification accuracy, which suggests that our network is more complete than a network based solely on protein interactions. We also showed that our network contains significantly more known cancer-related genes than other feature selection algorithms. Through observations of some examples of cancer-specific subnetworks, we were able to predict more detailed and interpretable roles of oncogenes and other cancer candidate genes in the prostate cancer cells.

AVAILABILITY

http://embio.yonsei.ac.kr/~Ahn/tc.php.

CONTACT

sanghyun@cs.yonsei.ac.kr

Unexpected complexity of the reef-building coral Acropora millepora transcription factor network

BACKGROUND

Coral reefs are disturbed on a global scale by environmental changes including rising sea surface temperatures and ocean acidification. Little is known about how corals respond or adapt to these environmental changes especially at the molecular level. This is mostly because of the paucity of genome-wide studies on corals and the application of systems approaches that incorporate the latter. Like in any other organism, the response of corals to stress is tightly controlled by the coordinated interplay of many transcription factors.

RESULTS

Here, we develop and apply a new system-wide approach in order to infer combinatorial transcription factor networks of the reef-building coral Acropora millepora. By integrating sequencing-derived transcriptome measurements, a network of physically interacting transcription factors, and phylogenetic network footprinting we were able to infer such a network. Analysis of the network across a phylogenetically broad sample of five species, including human, reveals that despite the apparent simplicity of corals, their transcription factors repertoire and interaction networks seem to be largely conserved. In addition, we were able to identify interactions among transcription factors that appear to be species-specific lending strength to the novel concept of "Taxonomically Restricted Interactions".

CONCLUSIONS

This study provides the first look at transcription factor networks in corals. We identified a transcription factor repertoire encoded by the coral genome and found consistencies of the domain architectures of transcription factors and conserved regulatory subnetworks across eumetazoan species, providing insight into how regulatory networks have evolved.

What does biologically meaningful mean? A perspective on gene regulatory network validation

Gene regulatory networks (GRNs) are rapidly being delineated, but their quality and biological meaning are often questioned. Here, I argue that biological meaning is challenging to define and discuss reasons why GRN validation should be interpreted cautiously.

Large scale RNAi screen reveals that the inhibitor of DNA binding 2 (ID2) protein is repressed by p53 family member p63 and functions in human keratinocyte differentiation

The inhibitor of DNA binding 2, dominant negative helix-loop-helix protein, ID2, acts as an oncogene and elevated levels of ID2 have been reported in several malignancies. Whereas some inducers of the ID2 gene have been characterized, little is known regarding the proteins capable to repress its expression. We developed siRNA microarrays to perform a large scale loss-of-function screen in human adult keratinocytes engineered to express GFP under the control of the upstream region of ID2 gene. We screened the effect of siRNA-dependent inhibition of 220 cancer-associated genes on the expression of the ID2::GFP reporter construct. Three genes NBN, RAD21, and p63 lead to a repression of ID2 promoter activity. Strikingly NBN and RAD21 are playing on major role in cell cycle progression and mitosis arrest. These results underline the pregnant need to silence ID2 expression at transcript level to promote cell cycle exit. Central to this inhibitory mechanism we find p63, a key transcription factor in epithelial development and differentiation, which binds specific cis-acting sequence within the ID2 gene promoter both in vitro and in vivo. P63 would not suppress ID2 expression, but would rather prevent excessive expression of that protein to enable the onset of keratinocyte differentiation.

PITX2c is expressed in the adult left atrium, and reducing Pitx2c expression promotes atrial fibrillation inducibility and complex changes in gene expression

BACKGROUND

Intergenic variations on chromosome 4q25, close to the PITX2 transcription factor gene, are associated with atrial fibrillation (AF). We therefore tested whether adult hearts express PITX2 and whether variation in expression affects cardiac function.

METHODS AND RESULTS

mRNA for PITX2 isoform c was expressed in left atria of human and mouse, with levels in right atrium and left and right ventricles being 100-fold lower. In mice heterozygous for Pitx2c (Pitx2c(+/-)), left atrial Pitx2c expression was 60% of wild-type and cardiac morphology and function were not altered, except for slightly elevated pulmonary flow velocity. Isolated Pitx2c(+/-) hearts were susceptible to AF during programmed stimulation. At short paced cycle lengths, atrial action potential durations were shorter in Pitx2c(+/-) than in wild-type. Perfusion with the β-receptor agonist orciprenaline abolished inducibility of AF and reduced the effect on action potential duration. Spontaneous heart rates, atrial conduction velocities, and activation patterns were not affected in Pitx2c(+/-) hearts, suggesting that action potential duration shortening caused wave length reduction and inducibility of AF. Expression array analyses comparing Pitx2c(+/-) with wild-type, for left atrial and right atrial tissue separately, identified genes related to calcium ion binding, gap and tight junctions, ion channels, and melanogenesis as being affected by the reduced expression of Pitx2c.

CONCLUSIONS

These findings demonstrate a physiological role for PITX2 in the adult heart and support the hypothesis that dysregulation of PITX2 expression can be responsible for susceptibility to AF.

Critical role of the Rb family in myoblast survival and fusion

The tumor suppressor Rb is thought to control cell proliferation, survival and differentiation. We recently showed that differentiating Rb-deficient mouse myoblasts can fuse to form short myotubes that quickly collapse through a mechanism involving autophagy, and that autophagy inhibitors or hypoxia could rescue the defect leading to long, twitching myotubes. Here we determined the contribution of pRb relatives, p107 and p130, to this process. We show that chronic or acute inactivation of Rb plus p107 or p130 increased myoblast cell death and reduced myotube formation relative to Rb loss alone. Treatment with autophagy antagonists or hypoxia extended survival of double-knockout myotubes, which appeared indistinguishable from control fibers. In contrast, triple mutations in Rb, p107 and p130, led to substantial increase in myoblast death and to elongated bi-nuclear myocytes, which seem to derive from nuclear duplication, as opposed to cell fusion. Under hypoxia, some rare, abnormally thin triple knockout myotubes survived and twitched. Thus, mutation of p107 or p130 reduces survival of Rb-deficient myoblasts during differentiation but does not preclude myoblast fusion or necessitate myotube degeneration, whereas combined inactivation of the entire Rb family produces a distinct phenotype, with drastically impaired myoblast fusion and survival.

Modeling human endometrial decidualization from the interaction between proteome and secretome

CONTEXT

Decidualization of the human endometrium, which involves morphological and biochemical modifications of the endometrial stromal cells (ESCs), is a prerequisite for adequate trophoblast invasion and placenta formation.

OBJECTIVE

This study aims to investigate the proteome and secretome of in vitro decidualized ESCs. These data were combined with published genomic information and integrated to model the human decidualization interactome.

DESIGN

Prospective experimental case-control study.

SETTING

A private research foundation.

PATIENTS

Sixteen healthy volunteer ovum donors.

INTERVENTION

Endometrial samples were obtained, and ESCs were isolated and decidualized in vitro.

MAIN OUTCOME MEASURES

Two-dimensional difference in-gel electrophoresis, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry, Western blot, human protein cytokine array, ELISA, and bioinformatics analysis were performed.

RESULTS

The proteomic analysis revealed 60 differentially expressed proteins (36 over- and 24 underexpressed) in decidualized versus control ESCs, including known decidualization markers (cathepsin B) and new biomarkers (transglutaminase 2, peroxiredoxin 4, and the ACTB protein). In the secretomic analysis, a total of 13 secreted proteins (11 up- and 2 down-regulated) were identified, including well-recognized markers (IGF binding protein-1 and prolactin) and novel ones (myeloid progenitor inhibitory factor-1 and platelet endothelial cell adhesion molecule-1). These proteome/secretome profiles have been integrated into a decidualization interactome model.

CONCLUSIONS

Proteomic and secretomic have been used as hypothesis-free approaches together with complex bioinformatics to model the human decidual interactome for the first time. We confirm previous knowledge, describe new molecules, and we have built up a model for human in vitro decidualization as invaluable tool for the diagnosis, therapy, and interpretation of biological phenomena.

A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle

Harsh tropical environments impose serious challenges on poorly adapted species. In beef cattle, tropical adaptation in the form of temperature and disease resistance, coupled with acclimatization to seasonal and limited forage, comes at a cost to production efficiency. Prominent among these costs is delayed onset of puberty, a challenging phenotype to manipulate through traditional breeding mechanisms. Recently, system biology approaches, including gene networks, have been applied to the genetic dissection of complex phenotypes. We aimed at developing and studying gene networks underlying cattle puberty. Our starting material comprises the association results of ~50,000 SNP on 22 traits, including age at puberty, and 2 cattle breed populations: Brahman (n = 843) and Tropical Composite (n = 866). We defined age at puberty as the age at first corpus luteum (AGECL). By capturing the genes harboring mutations minimally associated (P < 0.05) to AGECL or to a set of traits related with AGECL, we derived a gene network for each breed separately and a third network for the combined data set. At the intersection of the 3 networks, we identified candidate genes and pathways that were common to both breeds. Resulting from these analyses, we identified an enrichment of genes involved in axon guidance, cell adhesion, ErbB signaling, and glutamate activity, pathways that are known to affect pulsatile release of GnRH, which is necessary for the onset of puberty. Furthermore, we employed network connectivity and centrality parameters along with a regulatory impact factor metric to identify the key transcription factors (TF) responsible for the molecular regulation of puberty. As a novel finding, we report 5 TF (HIVEP3, TOX, EYA1, NCOA2, and ZFHX4) located in the network intersecting both breeds and interacting with other TF, forming a regulatory network that harmonizes with the recent literature of puberty. Finally, we support our network predictions with evidence derived from gene expression in hypothalamic tissue of adult cows.

The genomic landscapes of inflammation

Inflammation involves the activation of a highly coordinated gene expression program that is specific for the initial stimulus and occurs in a different manner in bystander parenchymal cells and professional immune system cells recruited to the inflamed site. Recent data demonstrate that developmental transcription factors like the macrophage fate-determining Pu.1 set the stage for the activity of ubiquitous transcription factors activated by inflammatory stimuli, like NF-kB, AP-1, and interferon regulatory factors (IRFs). The intersection of lineage-determining and stimulus-activated transcription factors at enhancers explains cell type specificity in inflammatory responses.

The architecture of gene regulatory variation across multiple human tissues: the MuTHER study

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis—MCTA) permits immediate replication of eQTLs using co-twins (93%–98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%–20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits.

Regulation of gene expression is a fundamental cellular process determining a large proportion of the phenotypic variance. Previous studies have identified genetic loci influencing gene expression levels (eQTLs), but the complexity of their tissue-specific properties has not yet been well-characterized. In this study, we perform cis-eQTL analysis in a unique matched co-twin design for three human tissues derived simultaneously from the same set of individuals. The study design allows validation of the substantial discoveries we make in each tissue. We explore in depth the tissue-dependent features of regulatory variants and estimate the proportions of shared and specific effects. We use continuous measures of eQTL sharing to circumvent the statistical power limitations of comparing direct overlap of eQTLs in multiple tissues. In this framework, we demonstrate that 30% of eQTLs are shared among tissues, while 29% are exclusively tissue-specific. Furthermore, we show that the fold change in expression between eQTL genotypic classes differs between tissues. Even among shared eQTLs, we report a substantial proportion (10%–20%) of significant tissue differences in magnitude of these effects. The complexities we highlight here are essential for understanding the impact of regulatory variants on complex traits.

Role of helix-loop-helix proteins during differentiation of erythroid cells

Helix-loop-helix (HLH) proteins play a profound role in the process of development and cellular differentiation. Among the HLH proteins expressed in differentiating erythroid cells are the ubiquitous proteins Myc, USF1, USF2, and TFII-I, as well as the hematopoiesis-specific transcription factor Tal1/SCL. All of these HLH proteins exhibit distinct functions during the differentiation of erythroid cells. For example, Myc stimulates the proliferation of erythroid progenitor cells, while the USF proteins and Tal1 regulate genes that specify the differentiated phenotype. This minireview summarizes the known activities of Myc, USF, TFII-I, and Tal11/SCL and discusses how they may function sequentially, cooperatively, or antagonistically in regulating expression programs during the differentiation of erythroid cells.

Evolution of transcription networks--lessons from yeasts

That regulatory evolution is important in generating phenotypic diversity was suggested soon after the discovery of gene regulation. In the past few decades, studies in animals have provided a number of examples in which phenotypic changes can be traced back to specific alterations in transcriptional regulation. Recent advances in DNA sequencing technology and functional genomics have stimulated a new wave of investigation in simple model organisms. In particular, several genome-wide comparative analyses of transcriptional circuits across different yeast species have been performed. These studies have revealed that transcription networks are remarkably plastic: large scale rewiring in which target genes move in and out of regulons through changes in cis-regulatory sequences appears to be a general phenomenon. Transcription factor substitution and the formation of new combinatorial interactions are also important contributors to the rewiring. In several cases, a transition through intermediates with redundant regulatory programs has been suggested as a mechanism through which rewiring can occur without a loss in fitness. Because the basic features of transcriptional regulation are deeply conserved, we speculate that large scale rewiring may underlie the evolution of complex phenotypes in multi-cellular organisms; if so, such rewiring may leave traceable changes in the genome from which the genetic basis of functional innovation can be detected.

Identification of residues in the N-terminal PAS domains important for dimerization of Arnt and AhR

The basic helix–loop–helix (bHLH).PAS dimeric transcription factors have crucial roles in development, stress response, oxygen homeostasis and neurogenesis. Their target gene specificity depends in part on partner protein choices, where dimerization with common partner Aryl hydrocarbon receptor nuclear translocator (Arnt) is an essential step towards forming active, DNA binding complexes. Using a new bacterial two-hybrid system that selects for loss of protein interactions, we have identified 22 amino acids in the N-terminal PAS domain of Arnt that are involved in heterodimerization with aryl hydrocarbon receptor (AhR). Of these, Arnt E163 and Arnt S190 were selective for the AhR/Arnt interaction, since mutations at these positions had little effect on Arnt dimerization with other bHLH.PAS partners, while substitution of Arnt D217 affected the interaction with both AhR and hypoxia inducible factor-1α but not with single minded 1 and 2 or neuronal PAS4. Arnt uses the same face of the N-terminal PAS domain for homo- and heterodimerization and mutational analysis of AhR demonstrated that the equivalent region is used by AhR when dimerizing with Arnt. These interfaces differ from the PAS β-scaffold surfaces used for dimerization between the C-terminal PAS domains of hypoxia inducible factor-2α and Arnt, commonly used for PAS domain interactions.

Measuring the evolutionary rewiring of biological networks

We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

Dynamic combinatorial interactions of RUNX1 and cooperating partners regulates megakaryocytic differentiation in cell line models

Specific interactions of transcription factors (TFs) with their targets are crucial for specifying gene expression programs during cell differentiation. How specificity is maintained despite limited selectivity of individual TF-DNA interactions is not fully understood. RUNX1 TF is among the most frequently mutated genes in human leukemia and an important regulator of megakaryopoiesis. We used megakaryocytic cell lines to characterize the network of RUNX1 targets and cooperating TFs in differentiating megakaryocytes and demonstrated how dynamic partnerships between RUNX1 and cooperating TFs facilitated regulatory plasticity and specificity during this process. After differentiation onset, RUNX1 directly activated a large number of genes through interaction with preexisting and de novo binding sites. Recruitment of RUNX1 to de novo occupied sites occurred at H3K4me1-marked preprogrammed enhancers. A significant number of these de novo bound sites lacked RUNX motif but were occupied by AP-1 TFs. Reciprocally, AP-1 TFs were up-regulated by RUNX1 after 12-O-tetradecanoylphorbol-13-acetate induction and recruited to RUNX1-occupied sites lacking AP-1 motifs. At other differentiation stages, additional combinatorial interactions occurred between RUNX1 and its coregulators, GATA1 and ETS. The findings suggest that in differentiating megakaryocytic cell lines, RUNX1 cooperates with GATA1, AP-1, and ETS to orchestrate cell-specific transcription programs through dynamic TF partnerships.

Multivariate analysis of regulatory SNPs: empowering personal genomics by considering cis-epistasis and heterogeneity

Understanding how genetic variants impact the regulation and expression of genes is important for forging mechanistic links between variants and phenotypes in personal genomics studies. In this work, we investigate statistical interactions among variants that alter gene expression and identify 79 genes showing highly significant interaction effects consistent with genetic heterogeneity. Of the 79 genes, 28 have been linked to phenotypes through previous genomic studies. We characterize the structural and statistical nature of these 79 cis-epistasis models, and show that interacting regulatory SNPs often lie far apart from each other and can be quite distant from the gene they regulate. By using cis-epistasis models that account for more variance in gene expression, investigators may improve the power and replicability of their genomics studies, and more accurately estimate an individual's gene expression level, improving phenotype prediction.

Interactions of transcription factors with chromatin

Sequence-specific transcription factors (TFs) play a central role in regulating transcription initiation by directing the recruitment and activity of the general transcription machinery and accessory factors. It is now well established that many of the effects exerted by TFs in eukaryotes are mediated through interactions with a host of coregulators that modify the chromatin state, resulting in a more open (in case of activation) or closed conformation (in case of repression). The relationship between TFs and chromatin is a two-way street, however, as chromatin can in turn influence the recognition and binding of target sequences by TFs. The aim of this chapter is to highlight how this dynamic interplay between TF-directed remodelling of chromatin and chromatin-adjusted targeting of TF binding determines where and how transcription is initiated, and to what degree it is productive.

Large scale comparison of global gene expression patterns in human and mouse

Background

It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress.

Results

In a principal component analysis (PCA) of combined data from human and mouse samples merged on orthologous probesets, samples largely form distinctive clusters based on their tissue sources when projected onto the top principal components. The most prominent groups are the nervous system, muscle/heart tissues, liver and cell lines. Despite the great differences in sample characteristics and experiment conditions, the overall patterns of these prominent clusters are strikingly similar for human and mouse. We further analyzed data for each tissue separately and found that the most variable genes in each tissue are highly enriched with human-mouse tissue-specific orthologs and the least variable genes in each tissue are enriched with human-mouse housekeeping orthologs.

Conclusions

The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse. The expression of groups of orthologous genes co-varies in the two species, both for the most variable genes and the most ubiquitously expressed genes.

Genome-wide binding of the orphan nuclear receptor TR4 suggests its general role in fundamental biological processes

Background

The orphan nuclear receptor TR4 (human testicular receptor 4 or NR2C2) plays a pivotal role in a variety of biological and metabolic processes. With no known ligand and few known target genes, the mode of TR4 function was unclear.

Results

We report the first genome-wide identification and characterization of TR4 in vivo binding. Using chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), we identified TR4 binding sites in 4 different human cell types and found that the majority of target genes were shared among different cells. TR4 target genes are involved in fundamental biological processes such as RNA metabolism and protein translation. In addition, we found that a subset of TR4 target genes exerts cell-type specific functions. Analysis of the TR4 binding sites revealed that less than 30% of the peaks from any of the cell types contained the DR1 motif previously derived from in vitro studies, suggesting that TR4 may be recruited to the genome via interaction with other proteins. A bioinformatics analysis of the TR4 binding sites predicted a cis regulatory module involving TR4 and ETS transcription factors. To test this prediction, we performed ChIP-seq for the ETS factor ELK4 and found that 30% of TR4 binding sites were also bound by ELK4. Motif analysis of the sites bound by both factors revealed a lack of the DR1 element, suggesting that TR4 binding at a subset of sites is facilitated through the ETS transcription factor ELK4. Further studies will be required to investigate the functional interdependence of these two factors.

Conclusions

Our data suggest that TR4 plays a pivotal role in fundamental biological processes across different cell types. In addition, the identification of cell type specific TR4 binding sites enables future studies of the pathways underlying TR4 action and its possible role in metabolic diseases.

High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells

Regulation of gene transcription in diverse cell types is determined largely by varied sets of cis-elements where transcription factors bind. Here we demonstrate that data from a single high-throughput DNase I hypersensitivity assay can delineate hundreds of thousands of base-pair resolution in vivo footprints in human cells that precisely mark individual transcription factor-DNA interactions. These annotations provide a unique resource for the investigation of cis-regulatory elements. We find that footprints for specific transcription factors correlate with ChIP-seq enrichment and can accurately identify functional versus nonfunctional transcription factor motifs. We also find that footprints reveal a unique evolutionary conservation pattern that differentiates functional footprinted bases from surrounding DNA. Finally, detailed analysis of CTCF footprints suggests multiple modes of binding and a novel DNA binding motif upstream of the primary binding site.

The RIKEN integrated database of mammals

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN's original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists' Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information.

Update of the FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation

The international Functional Annotation Of the Mammalian Genomes 4 (FANTOM4) research collaboration set out to better understand the transcriptional network that regulates macrophage differentiation and to uncover novel components of the transcriptome employing a series of high-throughput experiments. The primary and unique technique is cap analysis of gene expression (CAGE), sequencing mRNA 5′-ends with a second-generation sequencer to quantify promoter activities even in the absence of gene annotation. Additional genome-wide experiments complement the setup including short RNA sequencing, microarray gene expression profiling on large-scale perturbation experiments and ChIP–chip for epigenetic marks and transcription factors. All the experiments are performed in a differentiation time course of the THP-1 human leukemic cell line. Furthermore, we performed a large-scale mammalian two-hybrid (M2H) assay between transcription factors and monitored their expression profile across human and mouse tissues with qRT-PCR to address combinatorial effects of regulation by transcription factors. These interdependent data have been analyzed individually and in combination with each other and are published in related but distinct papers. We provide all data together with systematic annotation in an integrated view as resource for the scientific community (http://fantom.gsc.riken.jp/4/). Additionally, we assembled a rich set of derived analysis results including published predicted and validated regulatory interactions. Here we introduce the resource and its update after the initial release.

ConsensusPathDB: toward a more complete picture of cell biology

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human, 194 480 yeast and 13 648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

HiNO: an approach for inferring hierarchical organization from regulatory networks

Background

Gene expression as governed by the interplay of the components of regulatory networks is indeed one of the most complex fundamental processes in biological systems. Although several methods have been published to unravel the hierarchical structure of regulatory networks, weaknesses such as the incorrect or inconsistent assignment of elements to their hierarchical levels, the incapability to cope with cyclic dependencies within the networks or the need for a manual curation to retrieve non-overlapping levels remain unsolved.

Methodology/Results

We developed HiNO as a significant improvement of the so-called breadth-first-search (BFS) method. While BFS is capable of determining the overall hierarchical structures from gene regulatory networks, it especially has problems solving feed-forward type of loops leading to conflicts within the level assignments. We resolved these problems by adding a recursive correction approach consisting of two steps. First each vertex is placed on the lowest level that this vertex and its regulating vertices are assigned to (downgrade procedure). Second, vertices are assigned to the next higher level (upgrade procedure) if they have successors with the same level assignment and have themselves no regulators. We evaluated HiNO by comparing it with the BFS method by applying them to the regulatory networks from Saccharomyces cerevisiae and Escherichia coli, respectively. The comparison shows clearly how conflicts in level assignment are resolved in HiNO in order to produce correct hierarchical structures even on the local levels in an automated fashion.

Conclusions

We showed that the resolution of conflicting assignments clearly improves the BFS-method. While we restricted our analysis to gene regulatory networks, our approach is suitable to deal with any directed hierarchical networks structure such as the interaction of microRNAs or the action of non-coding RNAs in general. Furthermore we provide a user-friendly web-interface for HiNO that enables the extraction of the hierarchical structure of any directed regulatory network.

Availability

HiNO is freely accessible at http://mips.helmholtz-muenchen.de/hino/.

Transcription factor regulation of CD8+ T-cell memory and exhaustion

During an infection, antigen-specific CD8+ T cells undergo numerous cellular and transcriptional changes as they develop from naive T cells into effector and memory cells. However, when the antigen persists in a chronic infection, the cellular programs governing effector and memory development are influenced by chronic stimulation, and dysfunctional or exhausted CD8+ T cells are generated. Recently, exhausted CD8+ T cells were found to differ dramatically from naive and functional memory CD8+ T cells on a transcriptional level, demonstrating that exposure to chronic antigen can impact T cells at a fundamental level. While transcriptional changes in CD8+ T cells during memory development is currently a topic of particular interest, the transcriptional changes related to exhaustion and other forms of T-cell dysfunction have received less attention. New computational methods are not only uncovering important transcription factors in these developmental processes but are also going further to define and connect these transcription factors into transcriptional modules that work in parallel to control cell fate and state. Understanding the molecular processes behind the development of CD8+ T-cell memory and exhaustion should not only increase our understanding of the immune system but also could reveal therapeutic targets and treatments for infectious and immunological diseases. Here, we provide a basic overview of acute and chronic viral infections and the transcription factors known to influence the development of virus-specific T cells in both settings. We also discuss recent innovations in genomic and computational tools that could be used to enhance the way we understand the development of T-cell responses to infectious disease.

A decade of systems biology

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

MOTIVATION

Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space.

METHODS

'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering.

RESULTS

Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin.

CONCLUSION

MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal.

AVAILABILITY

https://sites.google.com/site/carlovittoriocannistraci/home.

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

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

Experimental strategies for studying transcription factor-DNA binding specificities

Specific binding of transcription factors (TFs) determines in a large part the connectivity of gene regulatory networks as well as the quantitative level of gene expression. A multiplicity of both experimental and computational methods is currently used to discover and characterize the underlying TF-DNA interactions. Experimental methods can be further subdivided into in vitro- and in vivo-based approaches, each accenting different aspects of TF-binding events. In this review we summarize the flexibility and performance of a selection of both types of experimental methods. In conclusion, we argue that a serial combination of methods with different throughput and data type constitutes an optimal experimental strategy.

Construction of a large extracellular protein interaction network and its resolution by spatiotemporal expression profiling

Extracellular interactions involving both secreted and membrane-tethered receptor proteins are essential to initiate signaling pathways that orchestrate cellular behaviors within biological systems. Because of the biochemical properties of these proteins and their interactions, identifying novel extracellular interactions remains experimentally challenging. To address this, we have recently developed an assay, AVEXIS (avidity-based extracellular interaction screen) to detect low affinity extracellular interactions on a large scale and have begun to construct interaction networks between zebrafish receptors belonging to the immunoglobulin and leucine-rich repeat protein families to identify novel signaling pathways important for early development. Here, we expanded our zebrafish protein library to include other domain families and many more secreted proteins and performed our largest screen to date totaling 16,544 potential unique interactions. We report 111 interactions of which 96 are novel and include the first documented extracellular ligands for 15 proteins. By including 77 interactions from previous screens, we assembled an expanded network of 188 extracellular interactions between 92 proteins and used it to show that secreted proteins have twice as many interaction partners as membrane-tethered receptors and that the connectivity of the extracellular network behaves as a power law. To try to understand the functional role of these interactions, we determined new expression patterns for 164 genes within our clone library by using whole embryo in situ hybridization at five key stages of zebrafish embryonic development. These expression data were integrated with the binding network to reveal where each interaction was likely to function within the embryo and were used to resolve the static interaction network into dynamic tissue- and stage-specific subnetworks within the developing zebrafish embryo. All these data were organized into a freely accessible on-line database called ARNIE (AVEXIS Receptor Network with Integrated Expression; www.sanger.ac.uk/arnie) and provide a valuable resource of new extracellular signaling interactions for developmental biology.

Identification of epistatic effects using a protein-protein interaction database

Epistasis (i.e. gene-gene interaction) has long been recognized as an important mechanism underlying the complexity of the genetic architecture of human traits. Definitions of epistasis range from the purely molecular to the traditional statistical measures of interaction. The statistical detection of epistasis usually does not map onto or easily relate to the biological interactions between genetic variations through their combined influence on gene expression or through their interactions at the gene product (i.e. protein) or DNA level. Recently, greater high-dimensional data on protein-protein interaction (PPI) and gene expression profiles have been collected that enumerates sets of biological interactions. To better align statistical and molecular models of epistasis, we present an example of how to incorporate the PPI information into the statistical analysis of interactions between copy number variations (CNVs). Among the 23 640 pairs of known human PPIs and the 1141 common CNVs detected among HapMap samples, we identified 37 pairs of CNVs overlapping with both genes of a PPI pair. Two CNV pairs provided sufficient genotype variation to search for epistatic effects on gene expression. Using 47 294 probe-specific gene expression levels as the outcomes, five epistatic effects were identified with P-value less than 10(-6). We found a CNV-CNV interaction significantly associated with gene expression of TP53TG3 (P-value of 2 × 10(-20)). The proteins associated with the CNV pair also bind TP53 which regulates the transcription of TP53TG3. This study demonstrates that using PPI data can assist in targeting statistical hypothesis testing to biological plausible epistatic interaction that reflects molecular mechanisms.

Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.

CRX ChIP-seq reveals the cis-regulatory architecture of mouse photoreceptors

Approximately 98% of mammalian DNA is noncoding, yet we understand relatively little about the function of this enigmatic portion of the genome. The cis-regulatory elements that control gene expression reside in noncoding regions and can be identified by mapping the binding sites of tissue-specific transcription factors. Cone-rod homeobox (CRX) is a key transcription factor in photoreceptor differentiation and survival, but its in vivo targets are largely unknown. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) on CRX to identify thousands of cis-regulatory regions around photoreceptor genes in adult mouse retina. CRX directly regulates downstream photoreceptor transcription factors and their target genes via a network of spatially distributed regulatory elements around each locus. CRX-bound regions act in a synergistic fashion to activate transcription and contain multiple CRX binding sites which interact in a spacing- and orientation-dependent manner to fine-tune transcript levels. CRX ChIP-seq was also performed on Nrl(-/-) retinas, which represent an enriched source of cone photoreceptors. Comparison with the wild-type ChIP-seq data set identified numerous rod- and cone-specific CRX-bound regions as well as many shared elements. Thus, CRX combinatorially orchestrates the transcriptional networks of both rods and cones by coordinating the expression of photoreceptor genes including most retinal disease genes. In addition, this study pinpoints thousands of noncoding regions of relevance to both Mendelian and complex retinal disease.

PriorsEditor: a tool for the creation and use of positional priors in motif discovery

Summary: Computational methods designed to discover transcription factor binding sites in DNA sequences often have a tendency to make a lot of false predictions. One way to improve accuracy in motif discovery is to rely on positional priors to focus the search to parts of a sequence that are considered more likely to contain functional binding sites. We present here a program called PriorsEditor that can be used to create such positional priors tracks based on a combination of several features, including phylogenetic conservation, nucleosome occupancy, histone modifications, physical properties of the DNA helix and many more.

Availability: PriorsEditor is available as a web start application and downloadable archive from http://tare.medisin.ntnu.no/priorseditor (requires Java 1.6). The web site also provides tutorials, screenshots and example protocol scripts.

Contact:kjetil.klepper@ntnu.no

The combination of gene perturbation assay and ChIP-chip reveals functional direct target genes for IRF8 in THP-1 cells

Gene regulatory networks in living cells are controlled by the interaction of multiple cell type-specific transcription regulators with DNA binding sites in target genes. Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence binding protein (ICSBP), is a transcription factor expressed predominantly in myeloid and lymphoid cell lineages. To find the functional direct target genes of IRF8, the gene expression profiles of siRNA knockdown samples and genome-wide binding locations by ChIP-chip were analyzed in THP-1 myelomonocytic leukemia cells. Consequently, 84 genes were identified as functional direct targets. The ETS family transcription factor PU.1, also known as SPI1, binds to IRF8 and regulates basal transcription in macrophages. Using the same approach, we identified 53 direct target genes of PU.1; these overlapped with 19 IRF8 targets. These 19 genes included key molecules of IFN signaling such as OAS1 and IRF9, but excluded other IFN-related genes amongst the IRF8 functional direct target genes. We suggest that IRF8 and PU.1 can have both combined, and independent actions on different promoters in myeloid cells.

Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo

Members of the large ETS family of transcription factors (TFs) have highly similar DNA-binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA-binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA-binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high-throughput microwell-based TF DNA-binding specificity assay, and protein-binding microarrays (PBMs). Both approaches reveal that the ETS-binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino-acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP-seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.