The functional consequences of variation in transcription factor binding

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Understanding how genetic variation between individuals determines variation in observable traits or disease risk is one of the core aims of genetics. It is known that genetic variation often affects gene regulatory DNA elements and directly causes variation in expression of nearby genes. This effect in turn cascades down to other genes via the complex pathways and gene interaction networks that ultimately govern how cells operate in an ever changing environment. In theory, when genetic variation and gene expression levels are measured simultaneously in a large number of individuals, the causal effects of genes on each other can be inferred using statistical models similar to those used in randomized controlled trials. We developed a novel method and ultra-fast software Findr which, unlike existing methods, takes into account the complex but unknown network context when predicting causality between specific gene pairs. Findr’s predictions have a significantly higher overlap with known gene networks compared to existing methods, using both simulated and real data. Findr is also nearly a million times faster, and hence the only software in its class that can handle modern datasets where the expression levels of ten-thousands of genes are simultaneously measured in hundreds to thousands of individuals.

Biological function in the twilight zone of sequence conservation

Strong DNA conservation among divergent species is an indicator of enduring functionality. With weaker sequence conservation we enter a vast ‘twilight zone’ in which sequence subject to transient or lower constraint cannot be distinguished easily from neutrally evolving, non-functional sequence. Twilight zone functional sequence is illuminated instead by principles of selective constraint and positive selection using genomic data acquired from within a species’ population. Application of these principles reveals that despite being biochemically active, most twilight zone sequence is not functional.

The haploinsufficient tumor suppressor, CUX1, acts as an analog transcriptional regulator that controls target genes through distal enhancers that loop to target promoters

One third of tumor suppressors are haploinsufficient transcriptional regulators, yet it remains unknown how a 50% reduction of a transcription factor is translated at the cis-regulatory level into a malignant transcriptional program. We studied CUX1, a haploinsufficient transcription factor that is recurrently mutated in hematopoietic and solid tumors. We determined CUX1 DNA-binding and target gene regulation in the wildtype and haploinsufficient states. CUX1 binds with transcriptional activators and cohesin at distal enhancers across three different human cell types. Haploinsufficiency of CUX1 altered the expression of a large number of genes, including cell cycle regulators, with concomitant increased cellular proliferation. Surprisingly, CUX1 occupancy decreased genome-wide in the haploinsufficient state, and binding site affinity did not correlate with differential gene expression. Instead, differentially expressed genes had multiple, low-affinity CUX1 binding sites, features of analog gene regulation. A machine-learning algorithm determined that chromatin accessibility, enhancer activity, and distance to the transcription start site are features of dose-sensitive CUX1 transcriptional regulation. Moreover, CUX1 is enriched at sites of DNA looping, as determined by Hi-C analysis, and these loops connect CUX1 to the promoters of regulated genes. We propose an analog model for haploinsufficient transcriptional deregulation mediated by higher order genome architecture.

Identification of genetic variants affecting vitamin D receptor binding and associations with autoimmune disease

Large numbers of statistically significant associations between sentinel SNPs and case-control status have been replicated by genome-wide association studies. Nevertheless, few underlying molecular mechanisms of complex disease are currently known. We investigated whether variation in binding of a transcription factor, the vitamin D receptor (VDR), whose activating ligand vitamin D has been proposed as a modifiable factor in multiple disorders, could explain any of these associations. VDR modifies gene expression by binding DNA as a heterodimer with the Retinoid X receptor (RXR). We identified 43,332 genetic variants significantly associated with altered VDR binding affinity (VDR-BVs) using a high-resolution (ChIP-exo) genome-wide analysis of 27 HapMap lymphoblastoid cell lines. VDR-BVs are enriched in consensus RXR::VDR binding motifs, yet most fell outside of these motifs, implying that genetic variation often affects the binding affinity only indirectly. Finally, we compared 341 VDR-BVs replicating by position in multiple individuals against background sets of variants lying within VDR-binding regions that had been matched in allele frequency and were independent with respect to linkage disequilibrium. In this stringent test, these replicated VDR-BVs were significantly (q < 0.1) and substantially (>2-fold) enriched in genomic intervals associated with autoimmune and other diseases, including inflammatory bowel disease, Crohn's disease and rheumatoid arthritis. The approach's validity is underscored by RXR::VDR motif sequence being predictive of binding strength and being evolutionarily constrained. Our findings are consistent with altered RXR::VDR binding contributing to immunity-related diseases. Replicated VDR-BVs associated with these disorders could represent causal disease risk alleles whose effect may be modifiable by vitamin D levels.

Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression

Over the past decade, a number of methods have emerged for inferring protein-level transcription factor activities in individual samples based on prior information about the structure of the gene regulatory network. We discuss how this has enabled new methods for dissecting trans-acting mechanisms that underpin genetic variation in gene expression.

The Neurospora Transcription Factor ADV-1 Transduces Light Signals and Temporal Information to Control Rhythmic Expression of Genes Involved in Cell Fusion

Light and the circadian clock have a profound effect on the biology of organisms through the regulation of large sets of genes. Toward understanding how light and the circadian clock regulate gene expression, we used genome-wide approaches to identify the direct and indirect targets of the light-responsive and clock-controlled transcription factor ADV-1 in Neurospora crassa A large proportion of ADV-1 targets were found to be light- and/or clock-controlled, and enriched for genes involved in development, metabolism, cell growth, and cell fusion. We show that ADV-1 is necessary for transducing light and/or temporal information to its immediate downstream targets, including controlling rhythms in genes critical to somatic cell fusion. However, while ADV-1 targets are altered in predictable ways in Δadv-1 cells in response to light, this is not always the case for rhythmic target gene expression. These data suggest that a complex regulatory network downstream of ADV-1 functions to generate distinct temporal dynamics of target gene expression relative to the central clock mechanism.

Most brain disease-associated and eQTL haplotypes are not located within transcription factor DNase-seq footprints in brain

Dense genotyping approaches have revealed much about the genetic architecture both of gene expression and disease susceptibility. However, assigning causality to genetic variants associated with a transcriptomic or phenotypic trait presents a far greater challenge. The development of epigenomic resources by ENCODE, the Epigenomic Roadmap and others has led to strategies that seek to infer the likely functional variants underlying these genome-wide association signals. It is known, for example, that such variants tend to be located within areas of open chromatin, as detected by techniques such as DNase-seq and FAIRE-seq. We aimed to assess what proportion of variants associated with phenotypic or transcriptomic traits in the human brain are located within transcription factor binding sites. The bioinformatic tools, Wellington and HINT, were used to infer transcription factor footprints from existing DNase-seq data derived from central nervous system tissues with high spatial resolution. This dataset was then employed to assess the likely contribution of altered transcription factor binding to both expression quantitative trait loci (eQTL) and genome-wide association study (GWAS) signals. Surprisingly, we show that most haplotypes associated with GWAS or eQTL phenotypes are located outside of DNase-seq footprints. This could imply that DNase-seq footprinting is too insensitive an approach to identify a large proportion of true transcription factor binding sites. Importantly, this suggests that prioritising variants for genome engineering studies to establish causality will continue to be frustrated by an inability of footprinting to identify the causative variant within a haplotype.

The Genetics of Transcription Factor DNA Binding Variation

Most complex trait-associated variants are located in non-coding regulatory regions of the genome, where they have been shown to disrupt transcription factor (TF)-DNA binding motifs. Variable TF-DNA interactions are therefore increasingly considered as key drivers of phenotypic variation. However, recent genome-wide studies revealed that the majority of variable TF-DNA binding events are not driven by sequence alterations in the motif of the studied TF. This observation implies that the molecular mechanisms underlying TF-DNA binding variation and, by extrapolation, inter-individual phenotypic variation are more complex than originally anticipated. Here, we summarize the findings that led to this important paradigm shift and review proposed mechanisms for local, proximal, or distal genetic variation-driven variable TF-DNA binding. In addition, we discuss the biomedical implications of these findings for our ability to dissect the molecular role(s) of non-coding genetic variants in complex traits, including disease susceptibility.

YY1 binding association with sex-biased transcription revealed through X-linked transcript levels and allelic binding analyses

Sex differences in susceptibility and progression have been reported in numerous diseases. Female cells have two copies of the X chromosome with X-chromosome inactivation imparting mono-allelic gene silencing for dosage compensation. However, a subset of genes, named escapees, escape silencing and are transcribed bi-allelically resulting in sexual dimorphism. Here we conducted in silico analyses of the sexes using human datasets to gain perspectives into such regulation. We identified transcription start sites of escapees (escTSSs) based on higher transcription levels in female cells using FANTOM5 CAGE data. Significant over-representations of YY1 transcription factor binding motif and ChIP-seq peaks around escTSSs highlighted its positive association with escapees. Furthermore, YY1 occupancy is significantly biased towards the inactive X (Xi) at long non-coding RNA loci that are frequent contacts of Xi-specific superloops. Our study suggests a role for YY1 in transcriptional activity on Xi in general through sequence-specific binding, and its involvement at superloop anchors.

Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests

Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC.

Distinct Cell Stress Responses Induced by ATP Restriction in Quiescent Human Fibroblasts

Quiescence is the prevailing state of many cell types under homeostatic conditions. Yet, surprisingly little is known about how quiescent cells respond to energetic and metabolic challenges. To better understand compensatory responses of quiescent cells to metabolic stress, we established, in human primary dermal fibroblasts, an experimental ‘energy restriction’ model. Quiescence was achieved by short-term culture in serum-deprived media and ATP supply restricted using a combination of glucose transport inhibitors and mitochondrial uncouplers. In aggregate, these measures led to markedly reduced intracellular ATP levels while not compromising cell viability over the observation period of 48 h. Analysis of the transcription factor (TF) landscape induced by this treatment revealed alterations in several signal transduction nodes beyond the expected biosynthetic adaptations. These included increased abundance of NF-κB regulated TFs and altered TF subsets regulated by Akt and p53. The observed changes in gene regulation and corresponding alterations in key signaling nodes are likely to contribute to cell survival at intracellular ATP concentrations substantially below those achieved by growth factor deprivation alone. This experimental model provides a benchmark for the investigation of cell survival pathways and related molecular targets that are associated with restricted energy supply associated with biological aging and metabolic diseases.

Mechanism of allele specific assembly and disruption of master regulator transcription factor complexes of NF-KBp50, NF-KBp65 and HIF1a on a non-coding FAS SNP

A challenging question in genetics is to understand the molecular function of non-coding variants of the genome. By using differential EMSA, ChIP and functional genome analysis, we have found that changes in transcription factors (TF) apparent binding affinity and dissociation rates are responsible for allele specific assembly or disruption of master TFs: we observed that NF-KBp50, NF-KBp65 and HIF1a bind with an affinity of up to 10 fold better to the C-allele than to the T-allele of rs7901656 both in vivo and in vitro. Furthermore, we showed that NF-KBp50, p65 and HIF1a form higher order heteromultimeric complexes overlapping rs7901656, implying synergism of action among TFs governing cellular response to infection and hypoxia. With rs7901656 on the FAS gene as a paradigm, we show how allele specific transcription factor complex assembly and disruption by a causal variant contributes to disease and phenotypic diversity. This finding provides the highly needed mechanistic insight into how the molecular etiology of regulatory SNPs can be understood in functional terms.

Ehrlichia chaffeensis TRP32 is a Nucleomodulin that Directly Regulates Expression of Host Genes Governing Differentiation and Proliferation

Ehrlichia chaffeensis is an obligately intracellular bacterium that reprograms the mononuclear phagocyte through diverse effector-host interactions to modulate numerous host cell processes, including transcription. In a previous study, we reported that E. chaffeensis TRP32, a type 1 secreted effector, interacts with multiple host nucleus-associated proteins and also auto-activates reporter gene expression in yeast. In this study, we demonstrate that TRP32 is a nucleomodulin that binds host DNA and alters host gene transcription. TRP32 enters the host cell nucleus via a noncanonical translocation mechanism that involves phosphorylation of Y179 located in a C-terminal tri-tyrosine motif. Both genistein and mutation of Y179 inhibited TRP32 nuclear entry. An electromobility shift assay (EMSA) demonstrated TRP32 host DNA binding via its tandem repeat domain. TRP32 DNA binding and motif preference were further confirmed by supershift assays, as well as competition and mutant probe analyses. Using ChIP-Seq, we determined that TRP32 binds a G-rich motif primarily within ±500 bp of the gene transcription start site. An ontology analysis identified genes involved in processes such as immune cell differentiation, chromatin remodeling, and RNA transcription and processing, as primary TRP32 targets. TRP32 bound genes (n=1223) were distributed on all chromosomes and included several global regulators of proliferation and inflammation such as FOS and JUN, AKT3 and NRAS, and non-coding RNA genes, miRNA 21 and miRNA 142. TRP32 target genes were differentially regulated during infection, the majority of which were repressed, and direct repression/activation of these genes by TRP32 was confirmed in vitro with a cellular luciferase reporter assay.

Characterisation of the global transcriptional response to heat shock and the impact of individual genetic variation

BACKGROUND

The heat shock transcriptional response is essential to effective cellular function under stress. This is a highly heritable trait but the nature and extent of inter-individual variation in heat shock response remains unresolved.

METHODS

We determined global transcription profiles of the heat shock response for a panel of lymphoblastoid cell lines established from 60 founder individuals in the Yoruba HapMap population. We explore the observed differentially expressed gene sets following heat shock, establishing functional annotations, underlying networks and nodal genes involving heat shock factor 1 recruitment. We define a multivariate phenotype for the global transcriptional response to heat shock using partial least squares regression and map this quantitative trait to associated genetic variation in search of the major genomic modulators.

RESULTS

A comprehensive dataset of differentially expressed genes following heat shock in humans is presented. We identify nodal genes downstream of heat shock factor 1 in this gene set, notably involving ubiquitin C and small ubiquitin-like modifiers together with transcription factors. We dissect a multivariate phenotype for the global heat shock response which reveals distinct clustering of individuals in terms of variance of the heat shock response and involves differential expression of genes involved in DNA replication and cell division in some individuals. We find evidence of genetic associations for this multivariate response phenotype that involves trans effects modulating expression of genes following heat shock, including HSF1 and UBQLN1.

CONCLUSION

This study defines gene expression following heat shock for a cohort of individuals, establishing insights into the biology of the heat shock response and hypotheses for how variation in this may be modulated by underlying genetic diversity.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections. An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell. However, our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete. To address this gap, we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs. We inferred regulatory networks for human cell line and mouse host systems, which recapitulated several known regulators and pathways of the immune response and viral life cycle. We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response. We predicted several novel regulators, both at the mRNA and protein levels, and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication.

Mechanisms of DNA-binding specificity and functional gene regulation by transcription factors

Eukaryotic transcription factors up-regulate and down-regulate the expression of genes in a very controlled manner. The DNA-binding domains of these proteins have quite well established mechanisms for binding to DNA, but a surprisingly poor intrinsic ability to discriminate target and variant non-target DNA sequences. Here, we summarise established mechanisms of protein-DNA recognition, as specified by both macromolecules. We also review recent advances in the fields of genome binding, molecular dynamics and biomolecular interaction studies that bring us close to a full understanding of how eukaryotic transcription factors find and target DNA in vivo to form functional centres of gene regulation through networks of protein-protein and protein-DNA interactions.

Binding Sites in the EFG1 Promoter for Transcription Factors in a Proposed Regulatory Network: A Functional Analysis in the White and Opaque Phases of Candida albicans

In Candida albicans the transcription factor Efg1, which is differentially expressed in the white phase of the white-opaque transition, is essential for expression of the white phenotype. It is one of six transcription factors included in a proposed interactive transcription network regulating white-opaque switching and maintenance of the alternative phenotypes. Ten sites were identified in the EFG1 promoter that differentially bind one or more of the network transcription factors in the white and/or opaque phase. To explore the functionality of these binding sites in the differential expression of EFG1, we generated targeted deletions of each of the 10 binding sites, combinatorial deletions, and regional deletions using a Renillareniformis luciferase reporter system. Individually targeted deletion of only four of the 10 sites had minor effects consistent with differential expression of EFG1, and only in the opaque phase. Alternative explanations are considered.

ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles

Chromatin immunoprecipitation with massively parallel DNA sequencing (ChIP-seq) has greatly improved the reliability with which transcription factor binding sites (TFBSs) can be identified from genome-wide profiling studies. Many computational tools are developed to detect binding events or peaks, however the robust detection of weak binding events remains a challenge for current peak calling tools. We have developed a novel Bayesian approach (ChIP-BIT) to reliably detect TFBSs and their target genes by jointly modeling binding signal intensities and binding locations of TFBSs. Specifically, a Gaussian mixture model is used to capture both binding and background signals in sample data. As a unique feature of ChIP-BIT, background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. Extensive simulation studies showed a significantly improved performance of ChIP-BIT in target gene prediction, particularly for detecting weak binding signals at gene promoter regions. We applied ChIP-BIT to find target genes from NOTCH3 and PBX1 ChIP-seq data acquired from MCF-7 breast cancer cells. TF knockdown experiments have initially validated about 30% of co-regulated target genes identified by ChIP-BIT as being differentially expressed in MCF-7 cells. Functional analysis on these genes further revealed the existence of crosstalk between Notch and Wnt signaling pathways.

Integrated analyses of gene expression and genetic association studies in a founder population

Genome-wide association studies (GWASs) have become a standard tool for dissecting genetic contributions to disease risk. However, these studies typically require extraordinarily large sample sizes to be adequately powered. Strategies that incorporate functional information alongside genetic associations have proved successful in increasing GWAS power. Following this paradigm, we present the results of 20 different genetic association studies for quantitative traits related to complex diseases, conducted in the Hutterites of South Dakota. To boost the power of these association studies, we collected RNA-sequencing data from lymphoblastoid cell lines for 431 Hutterite individuals. We then used Sherlock, a tool that integrates GWAS and expression quantitative trait locus (eQTL) data, to identify weak GWAS signals that are also supported by eQTL data. Using this approach, we found novel associations with quantitative phenotypes related to cardiovascular disease, including carotid intima-media thickness, left atrial volume index, monocyte count and serum YKL-40 levels.

Decreased Transcription Factor Binding Levels Nearby Primate Pseudogenes Suggest Regulatory Degeneration

Characteristics of pseudogene degeneration at the coding level are well-known, such as a shift toward neutral rates of nonsynonymous substitutions and gain of frameshift mutations. In contrast, degeneration of pseudogene transcriptional regulation is not well understood. Here, we test two predictions of regulatory degeneration along a pseudogenized lineage: 1) Decreased transcription factor (TF) binding and 2) accelerated evolution in putative cis-regulatory regions.We find evidence for decreased TF binding levels nearby two primate pseudogenes compared with functional liver genes. However, the majority of TF-bound sequences nearby pseudogenes do not show evidence for lineage-specific accelerated rates of evolution. We conclude that decreases in TF binding level could be a marker for regulatory degeneration, while sequence degeneration in primate cis-regulatory modules may be obscured by background rates of TF binding site turnover.

Functional transcription factor target discovery via compendia of binding and expression profiles

Genome-wide experiments to map the DNA-binding locations of transcription-associated factors (TFs) have shown that the number of genes bound by a TF far exceeds the number of possible direct target genes. Distinguishing functional from non-functional binding is therefore a major challenge in the study of transcriptional regulation. We hypothesized that functional targets can be discovered by correlating binding and expression profiles across multiple experimental conditions. To test this hypothesis, we obtained ChIP-seq and RNA-seq data from matching cell types from the human ENCODE resource, considered promoter-proximal and distal cumulative regulatory models to map binding sites to genes, and used a combination of linear and non-linear measures to correlate binding and expression data. We found that a high degree of correlation between a gene’s TF-binding and expression profiles was significantly more predictive of the gene being differentially expressed upon knockdown of that TF, compared to using binding sites in the cell type of interest only. Remarkably, TF targets predicted from correlation across a compendium of cell types were also predictive of functional targets in other cell types. Finally, correlation across a time course of ChIP-seq and RNA-seq experiments was also predictive of functional TF targets in that tissue.

Functionalities of expressed messenger RNAs revealed from mutant phenotypes

Total messenger RNAs mRNAs that are produced from a given gene under a certain set of conditions include both functional and nonfunctional transcripts. The high prevalence of nonfunctional mRNAs that have been detected in cells has raised questions regarding the functional implications of mRNA expression patterns and divergences. Phenotypes that result from the mutagenesis of protein-coding genes have provided the most straightforward descriptions of gene functions, and such data obtained from model organisms have facilitated investigations of the functionalities of expressed mRNAs. Mutant phenotype data from mouse tissues have revealed various attributes of functional mRNAs, including tissue-specificity, strength of expression, and evolutionary conservation. In addition, the role that mRNA expression evolution plays in driving morphological evolution has been revealed from studies designed to exploit morphological and physiological phenotypes of mouse mutants. Investigations into yeast essential genes (defined by an absence of colony growth after gene deletion) have further described gene regulatory strategies that reduce protein expression noise by mediating the rates of transcription and translation. In addition to the functional significance of expressed mRNAs as described in the abovementioned findings, the functionalities of other type of RNAs (i.e., noncoding RNAs) remain to be characterized with systematic mutations and phenotyping of the DNA regions that encode these RNA molecules. WIREs RNA 2016, 7:416-427. doi: 10.1002/wrna.1329 For further resources related to this article, please visit the WIREs website.

The genetic and regulatory architecture of ERBB3-type 1 diabetes susceptibility locus

The study aimed to explore the role of ERBB3 in type 1 diabetes (T1D). We examined whether genetic variation of ERBB3 (rs2292239) affects residual β-cell function in T1D cases. Furthermore, we examined the expression of ERBB3 in human islets, the effect of ERBB3 knockdown on apoptosis in insulin-producing INS-1E cells and the genetic and regulatory architecture of the ERBB3 locus to provide insights to how rs2292239 may confer disease susceptibility. rs2292239 strongly correlated with residual β-cell function and metabolic control in children with T1D. ERBB3 locus associated lncRNA (NONHSAG011351) was found to be expressed in human islets. ERBB3 was expressed and down-regulated by pro-inflammatory cytokines in human islets and INS-1E cells; knockdown of ERBB3 in INS-1E cells decreased basal and cytokine-induced apoptosis. Our data suggests an important functional role of ERBB3 and its potential regulators in the β-cells and may constitute novel targets to prevent β-cell destruction in T1D.

Functional redundancy of transcription factors explains why most binding targets of a transcription factor are not affected when the transcription factor is knocked out

Background

Biologists are puzzled by the extremely low percentage (3%) of the binding targets of a yeast transcription factor (TF) affected when the TF is knocked out, a phenomenon observed by comparing the TF binding dataset and TF knockout effect dataset.

Results

This study gives a plausible biological explanation of this counterintuitive phenomenon. Our analyses find that TFs with high functional redundancy show significantly lower percentage than do TFs with low functional redundancy. This suggests that functional redundancy may lead to one TF compensating for another, thus masking the TF knockout effect on the binding targets of the knocked-out TF. In addition, we show that seven classes of genes (lowly expressed genes, TATA box-less genes, genes containing a nucleosome-free region immediately upstream of the transcriptional start site (TSS), genes with low transcriptional plasticity, genes with a low number of bound TFs, genes with a low number of TFBSs, and genes with a short average distance of TFBSs to the TSS) are insensitive to the knockout of their promoter-binding TFs, providing clues for finding other biological explanations of the surprisingly low percentage of the binding targets of a TF affected when the TF is knocked out.

Conclusions

This study shows that one property of TFs (functional redundancy) and seven properties of genes (expression level, TATA box, nucleosome, transcriptional plasticity, the number of bound TFs, the number of TFBSs, and the average distance of TFBSs to the TSS) may be useful for explaining a counterintuitive phenomenon: most binding targets of a yeast transcription factor are not affected when the transcription factor is knocked out.

Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models

Cancer genomes contain vast amounts of somatic mutations, many of which are passenger mutations not involved in oncogenesis. Whereas driver mutations in protein-coding genes can be distinguished from passenger mutations based on their recurrence, non-coding mutations are usually not recurrent at the same position. Therefore, it is still unclear how to identify cis-regulatory driver mutations, particularly when chromatin data from the same patient is not available, thus relying only on sequence and expression information. Here we use machine-learning methods to predict functional regulatory regions using sequence information alone, and compare the predicted activity of the mutated region with the reference sequence. This way we define the Predicted Regulatory Impact of a Mutation in an Enhancer (PRIME). We find that the recently identified driver mutation in the TAL1 enhancer has a high PRIME score, representing a “gain-of-target” for MYB, whereas the highly recurrent TERT promoter mutation has a surprisingly low PRIME score. We trained Random Forest models for 45 cancer-related transcription factors, and used these to score variations in the HeLa genome and somatic mutations across more than five hundred cancer genomes. Each model predicts only a small fraction of non-coding mutations with a potential impact on the function of the encompassing regulatory region. Nevertheless, as these few candidate driver mutations are often linked to gains in chromatin activity and gene expression, they may contribute to the oncogenic program by altering the expression levels of specific oncogenes and tumor suppressor genes.

Precise regulation of gene expression is controlled by cis-regulatory modules (CRM) containing binding sites for transcription factors (TF). The genome-wide location of all TF binding sites can often be obtained by ChIP-seq (chromatin immunoprecipitation followed by deep sequencing), yet in most cases only a minority of the binding peaks actually represent functional CRMs that control the transcription initiation of a bona fide TF target gene. Here, we investigated for 45 cancer-related TFs how machine-learning approaches can be used to predict functional TF target CRMs. After careful evaluation of their performance, we used these TF-target classifiers to predict which cis-regulatory mutations may have a significant impact on gene regulation by evaluating whether the mutation causes a significant gain or loss in the probability that the CRM is a functional TF target. We found that Random Forest classifiers can achieve more than 100-fold higher specificity for mutation prediction compared to the simple approaches based on scanning with position weight matrices. By scanning somatic mutations in breast cancer genomes and in the HeLa genome, we finally show that our TF-target classifiers can identify high impact non-coding mutations that are associated with concordant TF binding, gene expression changes and chromatin activity. In conclusion, TF-specific Random Forest classifiers can be used to prioritize cis-regulatory mutations in cancer genomes with high accuracy.

A high-throughput RNA-seq approach to profile transcriptional responses

In recent years RNA-seq protocols have been developed to investigate a variety of biological problems by measuring the abundance of different RNAs. Many study designs involve performing expensive preliminary studies to screen or optimize experimental conditions. Testing a large number of conditions in parallel may be more cost effective. For example, analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g., some cell types may not respond to certain treatments). To circumvent these challenges, we have established a new two-step high-throughput RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g., largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more efficient allocation of resources for the deep sequencing of the most biologically relevant libraries in step two. We have applied this approach to study the response to 23 treatments in three lymphoblastoid cell lines demonstrating that it should also be useful for other high-throughput transcriptome profiling applications requiring iterative refinement or screening.

Positive correlation between ADAR expression and its targets suggests a complex regulation mediated by RNA editing in the human brain

A-to-I RNA editing by adenosine deaminases acting on RNA is a post-transcriptional modification that is crucial for normal life and development in vertebrates. RNA editing has been shown to be very abundant in the human transcriptome, specifically at the primate-specific Alu elements. The functional role of this wide-spread effect is still not clear; it is believed that editing of transcripts is a mechanism for their down-regulation via processes such as nuclear retention or RNA degradation. Here we combine 2 neural gene expression datasets with genome-level editing information to examine the relation between the expression of ADAR genes with the expression of their target genes. Specifically, we computed the spatial correlation across structures of post-mortem human brains between ADAR and a large set of targets that were found to be edited in their Alu repeats. Surprisingly, we found that a large fraction of the edited genes are positively correlated with ADAR, opposing the assumption that editing would reduce expression. When considering the correlations between ADAR and its targets over development, 2 gene subsets emerge, positively correlated and negatively correlated with ADAR expression. Specifically, in embryonic time points, ADAR is positively correlated with many genes related to RNA processing and regulation of gene expression. These findings imply that the suggested mechanism of regulation of expression by editing is probably not a global one; ADAR expression does not have a genome wide effect reducing the expression of editing targets. It is possible, however, that RNA editing by ADAR in non-coding regions of the gene might be a part of a more complex expression regulation mechanism.

Evolution of DNA-Binding Sites of a Floral Master Regulatory Transcription Factor

Flower development is controlled by the action of key regulatory transcription factors of the MADS-domain family. The function of these factors appears to be highly conserved among species based on mutant phenotypes. However, the conservation of their downstream processes is much less well understood, mostly because the evolutionary turnover and variation of their DNA-binding sites (BSs) among plant species have not yet been experimentally determined. Here, we performed comparative ChIP (chromatin immunoprecipitation)-seq experiments of the MADS-domain transcription factor SEPALLATA3 (SEP3) in two closely related Arabidopsis species: Arabidopsis thaliana and A. lyrata which have very similar floral organ morphology. We found that BS conservation is associated with DNA sequence conservation, the presence of the CArG-box BS motif and on the relative position of the BS to its potential target gene. Differences in genome size and structure can explain that SEP3 BSs in A. lyrata can be located more distantly to their potential target genes than their counterparts in A. thaliana. In A. lyrata, we identified transposition as a mechanism to generate novel SEP3 binding locations in the genome. Comparative gene expression analysis shows that the loss/gain of BSs is associated with a change in gene expression. In summary, this study investigates the evolutionary dynamics of DNA BSs of a floral key-regulatory transcription factor and explores factors affecting this phenomenon.

Genetic Variation Determines PPARγ Function and Anti-diabetic Drug Response In Vivo

SNPs affecting disease risk often reside in non-coding genomic regions. Here, we show that SNPs are highly enriched at mouse strain-selective adipose tissue binding sites for PPARγ, a nuclear receptor for anti-diabetic drugs. Many such SNPs alter binding motifs for PPARγ or cooperating factors and functionally regulate nearby genes whose expression is strain selective and imbalanced in heterozygous F1 mice. Moreover, genetically determined binding of PPARγ accounts for mouse strain-specific transcriptional effects of TZD drugs, providing proof of concept for personalized medicine related to nuclear receptor genomic occupancy. In human fat, motif-altering SNPs cause differential PPARγ binding, provide a molecular mechanism for some expression quantitative trait loci, and are risk factors for dysmetabolic traits in genome-wide association studies. One PPARγ motif-altering SNP is associated with HDL levels and other metabolic syndrome parameters. Thus, natural genetic variation in PPARγ genomic occupancy determines individual disease risk and drug response.

Genome-wide errant targeting by Hairy

Metazoan transcriptional repressors regulate chromatin through diverse histone modifications. Contributions of individual factors to the chromatin landscape in development is difficult to establish, as global surveys reflect multiple changes in regulators. Therefore, we studied the conserved Hairy/Enhancer of Split family repressor Hairy, analyzing histone marks and gene expression in Drosophila embryos. This long-range repressor mediates histone acetylation and methylation in large blocks, with highly context-specific effects on target genes. Most strikingly, Hairy exhibits biochemical activity on many loci that are uncoupled to changes in gene expression. Rather than representing inert binding sites, as suggested for many eukaryotic factors, many regions are targeted errantly by Hairy to modify the chromatin landscape. Our findings emphasize that identification of active cis-regulatory elements must extend beyond the survey of prototypical chromatin marks. We speculate that this errant activity may provide a path for creation of new regulatory elements, facilitating the evolution of novel transcriptional circuits.

Population Variation and Genetic Control of Modular Chromatin Architecture in Humans

Chromatin state variation at gene regulatory elements is abundant across individuals, yet we understand little about the genetic basis of this variability. Here, we profiled several histone modifications, the transcription factor (TF) PU.1, RNA polymerase II, and gene expression in lymphoblastoid cell lines from 47 whole-genome sequenced individuals. We observed that distinct cis-regulatory elements exhibit coordinated chromatin variation across individuals in the form of variable chromatin modules (VCMs) at sub-Mb scale. VCMs were associated with thousands of genes and preferentially cluster within chromosomal contact domains. We mapped strong proximal and weak, yet more ubiquitous, distal-acting chromatin quantitative trait loci (cQTL) that frequently explain this variation. cQTLs were associated with molecular activity at clusters of cis-regulatory elements and mapped preferentially within TF-bound regions. We propose that local, sequence-independent chromatin variation emerges as a result of genetic perturbations in cooperative interactions between cis-regulatory elements that are located within the same genomic domain.

Ground State Conditions Induce Rapid Reorganization of Core Pluripotency Factor Binding before Global Epigenetic Reprogramming

Mouse embryonic stem cells (mESCs) cultured under serum/LIF conditions exhibit heterogeneous expression of pluripotency-associated factors that can be overcome by two inhibitors (2i) of the MEK and GSK3 pathways. Several studies have shown that the "ground state" induced by 2i is characterized by global hypomethylation and specific transcriptional profiles, but little is known about the contributing effectors. Here we show that 2i conditions rapidly alter the global binding landscape of OCT4, SOX2, and NANOG. The dynamic binding influences enhancer activity and shows enrichment for regulators linked to Wnt and Erk signaling. Epigenomic characterization provided limited insights to the immediate transcriptional dynamics, suggesting that these are likely more secondary effects. Likewise, loss of the PRC2 component EED to prevent H3K27me3 deposition had minimal effect on the transcriptome, implying that it is largely dispensable for continued repression of bivalent genes and de novo silencing in 2i.

Negative regulation of ABA signaling by WRKY33 is critical for Arabidopsis immunity towards Botrytis cinerea 2100

The Arabidopsis mutant wrky33 is highly susceptible to Botrytis cinerea. We identified &gt;1680 Botrytis-induced WRKY33 binding sites associated with 1576 Arabidopsis genes. Transcriptional profiling defined 318 functional direct target genes at 14 hr post inoculation. Comparative analyses revealed that WRKY33 possesses dual functionality acting either as a repressor or as an activator in a promoter-context dependent manner. We confirmed known WRKY33 targets involved in hormone signaling and phytoalexin biosynthesis, but also uncovered a novel negative role of abscisic acid (ABA) in resistance towards B. cinerea 2100. The ABA biosynthesis genes NCED3 and NCED5 were identified as direct targets required for WRKY33-mediated resistance. Loss-of-WRKY33 function resulted in elevated ABA levels and genetic studies confirmed that WRKY33 acts upstream of NCED3/NCED5 to negatively regulate ABA biosynthesis. This study provides the first detailed view of the genome-wide contribution of a specific plant transcription factor in modulating the transcriptional network associated with plant immunity.

Negative regulation of ABA signaling by WRKY33 is critical for Arabidopsis immunity towards Botrytis cinerea 2100

The Arabidopsis mutant wrky33 is highly susceptible to Botrytis cinerea. We identified &gt;1680 Botrytis-induced WRKY33 binding sites associated with 1576 Arabidopsis genes. Transcriptional profiling defined 318 functional direct target genes at 14 hr post inoculation. Comparative analyses revealed that WRKY33 possesses dual functionality acting either as a repressor or as an activator in a promoter-context dependent manner. We confirmed known WRKY33 targets involved in hormone signaling and phytoalexin biosynthesis, but also uncovered a novel negative role of abscisic acid (ABA) in resistance towards B. cinerea 2100. The ABA biosynthesis genes NCED3 and NCED5 were identified as direct targets required for WRKY33-mediated resistance. Loss-of-WRKY33 function resulted in elevated ABA levels and genetic studies confirmed that WRKY33 acts upstream of NCED3/NCED5 to negatively regulate ABA biosynthesis. This study provides the first detailed view of the genome-wide contribution of a specific plant transcription factor in modulating the transcriptional network associated with plant immunity.

Negative regulation of ABA signaling by WRKY33 is critical for Arabidopsis immunity towards Botrytis cinerea 2100

The Arabidopsis mutant wrky33 is highly susceptible to Botrytis cinerea. We identified &gt;1680 Botrytis-induced WRKY33 binding sites associated with 1576 Arabidopsis genes. Transcriptional profiling defined 318 functional direct target genes at 14 hr post inoculation. Comparative analyses revealed that WRKY33 possesses dual functionality acting either as a repressor or as an activator in a promoter-context dependent manner. We confirmed known WRKY33 targets involved in hormone signaling and phytoalexin biosynthesis, but also uncovered a novel negative role of abscisic acid (ABA) in resistance towards B. cinerea 2100. The ABA biosynthesis genes NCED3 and NCED5 were identified as direct targets required for WRKY33-mediated resistance. Loss-of-WRKY33 function resulted in elevated ABA levels and genetic studies confirmed that WRKY33 acts upstream of NCED3/NCED5 to negatively regulate ABA biosynthesis. This study provides the first detailed view of the genome-wide contribution of a specific plant transcription factor in modulating the transcriptional network associated with plant immunity.

High throughput technologies for the functional discovery of mammalian enhancers: new approaches for understanding transcriptional regulatory network dynamics

Completion of the human and mouse genomes has inspired new initiatives to obtain a global understanding of the functional regulatory networks governing gene expression. Enhancers are primary regulatory DNA elements determining precise spatio- and temporal gene expression patterns, but the observation that they can function at any distance from the gene(s) they regulate has made their genome-wide characterization challenging. Since traditional, single reporter approaches would be unable to accomplish this enormous task, high throughput technologies for mapping chromatin features associated with enhancers have emerged as an effective surrogate for enhancer discovery. However, the last few years have witnessed the development of several new innovative approaches that can effectively screen for and discover enhancers based on their functional activation of transcription using massively parallel reporter systems. In addition to their application for genome annotation, these new high throughput functional approaches open new and exciting avenues for modeling gene regulatory networks.

Understanding how cis-regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences

Genome-scale methods have identified thousands of candidate cis-regulatory elements (CREs), but methods to directly assay the regulatory function of these elements on a comparably large scale have not been available. The inability to directly test and perturb the regulatory activity of large numbers of DNA sequences has hindered efforts to discover how cis-regulatory function is encoded in genomic sequence. Recently developed massively parallel reporter gene assays combine next generation sequencing with high-throughput oligonucleotide synthesis to offer the capacity to test and mutationally perturb thousands of specifically chosen or designed cis-regulatory sequences in a single experiment. These assays are the basis of recent studies that include large-scale functional validation of genomic CREs, exhaustive mutational analyses of individual regulatory sequences, and tests of large libraries of synthetic CREs. The results demonstrate how massively parallel reporter assays with libraries of designed sequences provide the statistical power required to address previously intractable questions about cis-regulatory function.

Global transcription network incorporating distal regulator binding reveals selective cooperation of cancer drivers and risk genes

Global network modeling of distal regulatory interactions is essential in understanding the overall architecture of gene expression programs. Here, we developed a Bayesian probabilistic model and computational method for global causal network construction with breast cancer as a model. Whereas physical regulator binding was well supported by gene expression causality in general, distal elements in intragenic regions or loci distant from the target gene exhibited particularly strong functional effects. Modeling the action of long-range enhancers was critical in recovering true biological interactions with increased coverage and specificity overall and unraveling regulatory complexity underlying tumor subclasses and drug responses in particular. Transcriptional cancer drivers and risk genes were discovered based on the network analysis of somatic and genetic cancer-related DNA variants. Notably, we observed that the risk genes were functionally downstream of the cancer drivers and were selectively susceptible to network perturbation by tumorigenic changes in their upstream drivers. Furthermore, cancer risk alleles tended to increase the susceptibility of the transcription of their associated genes. These findings suggest that transcriptional cancer drivers selectively induce a combinatorial misregulation of downstream risk genes, and that genetic risk factors, mostly residing in distal regulatory regions, increase transcriptional susceptibility to upstream cancer-driving somatic changes.

Identification of active transcriptional regulatory elements from GRO-seq data

Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.

Cis-regulatory somatic mutations and gene-expression alteration in B-cell lymphomas

BACKGROUND

With the rapid increase of whole-genome sequencing of human cancers, an important opportunity to analyze and characterize somatic mutations lying within cis-regulatory regions has emerged. A focus on protein-coding regions to identify nonsense or missense mutations disruptive to protein structure and/or function has led to important insights; however, the impact on gene expression of mutations lying within cis-regulatory regions remains under-explored. We analyzed somatic mutations from 84 matched tumor-normal whole genomes from B-cell lymphomas with accompanying gene expression measurements to elucidate the extent to which these cancers are disrupted by cis-regulatory mutations.

RESULTS

We characterize mutations overlapping a high quality set of well-annotated transcription factor binding sites (TFBSs), covering a similar portion of the genome as protein-coding exons. Our results indicate that cis-regulatory mutations overlapping predicted TFBSs are enriched in promoter regions of genes involved in apoptosis or growth/proliferation. By integrating gene expression data with mutation data, our computational approach culminates with identification of cis-regulatory mutations most likely to participate in dysregulation of the gene expression program. The impact can be measured along with protein-coding mutations to highlight key mutations disrupting gene expression and pathways in cancer.

CONCLUSIONS

Our study yields specific genes with disrupted expression triggered by genomic mutations in either the coding or the regulatory space. It implies that mutated regulatory components of the genome contribute substantially to cancer pathways. Our analyses demonstrate that identifying genomically altered cis-regulatory elements coupled with analysis of gene expression data will augment biological interpretation of mutational landscapes of cancers.

Characterization of regulatory sequences in alternative promoters of hypermethylated genes associated with tumor resistance to cisplatin

The development of cisplatin resistance in human cancers is controlled by multiple genes and leads to therapeutic failure. Hypermethylation of specific gene promoters is a key event in clinical resistance to cisplatin. Although the usage of multiple promoters is frequent in the transcription of human genes, the role of alternative promoters and their regulatory sequences have not yet been investigated in cisplatin resistance genes. In a new approach, we hypothesized that human cancers exploit the specific transcription factor-binding sites (TFBS) and CpG islands (CGIs) located in the alternative promoters of certain genes to acquire platinum drug resistance. To provide a useful resource of regulatory elements associated with cisplatin resistance, we investigated the TFBS and CGIs in 48 alternative promoters of 14 hypermethylated cisplatin resistance genes previously reported. CGIs prone to methylation were identified in 28 alternative promoters of 11 hypermethylated genes. The majority of alternative promoters harboring CGIs (93%) were clustered in one phylogenetic subclass, whereas the ones lacking CGIs were distributed in two unrelated subclasses. Regulatory sequences, initiator and TATA-532 prevailed over TATA-8 and were found in all the promoters. B recognition element (BRE) sequences were present only in alternative promoters harboring CGIs, but CCAAT and TAACC were found in both types of alternative promoters, whereas downstream promoter element sequences were significantly less frequent. Therefore, it was hypothesized that BRE and CGI sequences co-localized in alternative promoters of cisplatin resistance genes may be used to design molecular markers for drug resistance. A more extensive knowledge of alternative promoters and their regulatory elements in clinical resistance to cisplatin is likely to usher novel avenues for sensitizing human cancers to treatment.

Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles

Much research on transcription factor biology and their genetic pathways has been undertaken over the last 30 years, especially in the field of developmental biology and cancer. Yet, very little is known about the molecular modalities of highly dynamic interactions between transcription factors, genomic DNA, and protein partners. Methodological breakthroughs such as RNA-seq (RNA-sequencing), ChIP-seq (chromatin immunoprecipitation sequencing), RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins), and single-molecule imaging will dramatically accelerate the discovery rate of their molecular mode of action in the next few years. From a pharmacological viewpoint, conventional methods used to target transcription factor activity with molecules mimicking endogenous ligands fail to achieve high specificity and are limited by a lack of identification of new molecular targets. Protein-protein interactions are likely to represent one of the next major classes of therapeutic targets. Transcription factors, known to act mostly via protein-protein interaction, may well be at the forefront of this type of drug development. One hurdle in this field remains the difficulty to collate structural data into meaningful information for rational drug design. Another hurdle is the lack of chemical libraries meeting the structural requirements of protein-protein interaction disruption. As more attempts at modulating transcription factor activity are undertaken, valuable knowledge will be accumulated on the modality of action required to modulate transcription and how these findings can be applied to developing transcription factor drugs. Key discoveries will spawn into new therapeutic approaches not only as anticancer targets but also for other indications, such as those with an inflammatory component including neurodegenerative disorders, diabetes, and chronic liver and kidney diseases.

Re-thinking miRNA-mRNA interactions: intertwining issues confound target discovery

Despite a library full of literature on miRNA biology, core issues relating to miRNA target detection, biological effect, and mode of action remain controversial. This essay proposes that the predominant mechanism of direct miRNA action is translational inhibition, whereas the bulk of miRNA effects are mRNA based. It explores several issues confounding miRNA target detection, and discusses their impact on the dominance of “miRNA seed” dogma and the exploration of non-canonical binding sites. Finally, it makes comparisons between miRNA target prediction and transcription factor binding prediction, and questions the value of characterizing miRNA binding sites based on which miRNA nucleotides are paired with an mRNA.

Decoupling of evolutionary changes in transcription factor binding and gene expression in mammals

To understand the evolutionary dynamics between transcription factor (TF) binding and gene expression in mammals, we compared transcriptional output and the binding intensities for three tissue-specific TFs in livers from four closely related mouse species. For each transcription factor, TF-dependent genes and the TF binding sites most likely to influence mRNA expression were identified by comparing mRNA expression levels between wild-type and TF knockout mice. Independent evolution was observed genome-wide between the rate of change in TF binding and the rate of change in mRNA expression across taxa, with the exception of a small number of TF-dependent genes. We also found that binding intensities are preferentially conserved near genes whose expression is dependent on the TF, and the conservation is shared among binding peaks in close proximity to each other near the TSS. Expression of TF-dependent genes typically showed an increased sensitivity to changes in binding levels as measured by mRNA abundance. Taken together, these results highlight a significant tolerance to evolutionary changes in TF binding intensity in mammalian transcriptional networks and suggest that some TF-dependent genes may be largely regulated by a single TF across evolution.

The genetic and mechanistic basis for variation in gene regulation

It is now well established that noncoding regulatory variants play a central role in the genetics of common diseases and in evolution. However, until recently, we have known little about the mechanisms by which most regulatory variants act. For instance, what types of functional elements in DNA, RNA, or proteins are most often affected by regulatory variants? Which stages of gene regulation are typically altered? How can we predict which variants are most likely to impact regulation in a given cell type? Recent studies, in many cases using quantitative trait loci (QTL)-mapping approaches in cell lines or tissue samples, have provided us with considerable insight into the properties of genetic loci that have regulatory roles. Such studies have uncovered novel biochemical regulatory interactions and led to the identification of previously unrecognized regulatory mechanisms. We have learned that genetic variation is often directly associated with variation in regulatory activities (namely, we can map regulatory QTLs, not just expression QTLs [eQTLs]), and we have taken the first steps towards understanding the causal order of regulatory events (for example, the role of pioneer transcription factors). Yet, in most cases, we still do not know how to interpret overlapping combinations of regulatory interactions, and we are still far from being able to predict how variation in regulatory mechanisms is propagated through a chain of interactions to eventually result in changes in gene expression profiles.

Genomic perspectives of transcriptional regulation in forebrain development

The forebrain is the seat of higher-order brain functions, and many human neuropsychiatric disorders are due to genetic defects affecting forebrain development, making it imperative to understand the underlying genetic circuitry. Recent progress now makes it possible to begin fully elucidating the genomic regulatory mechanisms that control forebrain gene expression. Herein, we discuss the current knowledge of how transcription factors drive gene expression programs through their interactions with cis-acting genomic elements, such as enhancers; how analyses of chromatin and DNA modifications provide insights into gene expression states; and how these approaches yield insights into the evolution of the human brain.

VSEAMS: a pipeline for variant set enrichment analysis using summary GWAS data identifies IKZF3, BATF and ESRRA as key transcription factors in type 1 diabetes

MOTIVATION

Genome-wide association studies (GWAS) have identified many loci implicated in disease susceptibility. Integration of GWAS summary statistics (P-values) and functional genomic datasets should help to elucidate mechanisms.

RESULTS

We extended a non-parametric SNP set enrichment method to test for enrichment of GWAS signals in functionally defined loci to a situation where only GWAS P-values are available. The approach is implemented in VSEAMS, a freely available software pipeline. We use VSEAMS to identify enrichment of type 1 diabetes (T1D) GWAS associations near genes that are targets for the transcription factors IKZF3, BATF and ESRRA. IKZF3 lies in a known T1D susceptibility region, while BATF and ESRRA overlap other immune disease susceptibility regions, validating our approach and suggesting novel avenues of research for T1D.

AVAILABILITY AND IMPLEMENTATION

VSEAMS is available for download (http://github.com/ollyburren/vseams).

The long non-coding RNA Dali is an epigenetic regulator of neural differentiation

Many intergenic long noncoding RNA (lncRNA) loci regulate the expression of adjacent protein coding genes. Less clear is whether intergenic lncRNAs commonly regulate transcription by modulating chromatin at genomically distant loci. Here, we report both genomically local and distal RNA-dependent roles of Dali, a conserved central nervous system expressed intergenic lncRNA. Dali is transcribed downstream of the Pou3f3 transcription factor gene and its depletion disrupts the differentiation of neuroblastoma cells. Locally, Dali transcript regulates transcription of the Pou3f3 locus. Distally, it preferentially targets active promoters and regulates expression of neural differentiation genes, in part through physical association with the POU3F3 protein. Dali interacts with the DNMT1 DNA methyltransferase in mouse and human and regulates DNA methylation status of CpG island-associated promoters in trans. These results demonstrate, for the first time, that a single intergenic lncRNA controls the activity and methylation of genomically distal regulatory elements to modulate large-scale transcriptional programmes.

Evolutionary dynamics of coding and non-coding transcriptomes

Gene expression changes may underlie much of phenotypic evolution. The development of high-throughput RNA sequencing protocols has opened the door to unprecedented large-scale and cross-species transcriptome comparisons by allowing accurate and sensitive assessments of transcript sequences and expression levels. Here, we review the initial wave of the new generation of comparative transcriptomic studies in mammals and vertebrate outgroup species in the context of earlier work. Together with various large-scale genomic and epigenomic data, these studies have unveiled commonalities and differences in the dynamics of gene expression evolution for various types of coding and non-coding genes across mammalian lineages, organs, developmental stages, chromosomes and sexes. They have also provided intriguing new clues to the regulatory basis and phenotypic implications of evolutionary gene expression changes.