A genome-wide survey reveals that a diverse array of enhancers coordinates the Drosophila innate immune response.

To defend against microbes, animals regulate a complex immune response. The Drosophila innate immune system deploys a large transcriptional induction of signaling proteins, antimicrobial effectors, and other critical immune factors. This transcriptional response is encoded in enhancers, cis-regulatory sequences that modulate gene expression by binding transcription factors (TFs). While enhancers and transcription factor binding sites (TFBS) have been identified for several immune responsive genes in Drosophila, most enhancers that regulate immune-induced genes are unknown. By identifying enhancers, we can understand how their composition controls expression and contributes to infection outcome. We employed STARR-seq (Self Transcribing Active Regulatory-Region sequencing) in a hemocyte-like cell line to identify immune-specific enhancers across the D. melanogaster genome and performed ATAC-seq in hemocytes extracted from adult flies to assess the chromatin state of these enhancers before and after immune stimulus. We identified thousands of enhancers responsive to IMD stimulation, one of the two primary immune signaling pathways in Drosophila. As expected, immune enhancers are enriched for motifs of Relish, an NF-κB factor, and Kay/Jra, a bZip heterodimer pair, involved in the Imd and JNK pathways respectively, compared to enhancers active in unstimulated cells. However, when grouping enhancers by their target gene’s expression timing or functional role or by the enhancers’ chromatin accessibility pre- or post-stimulus, different groups of TFBS motifs are enriched, suggesting distinct regulatory logic for different parts of the immune response. Identification and characterization of the diverse array of enhancers that regulate the innate immune response expands our understanding of how animals fight infections.

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools.

A flexible transcription initiation architecture and a uniform regulatory grammar buffer expression variability and the effects of genetic variants on essential genes while a rigid transcription initiation architecture and a diverse regulatory grammar ensure condition-specific responsiveness of other genes.

L1 transposons occupy 17% of the human genome and are widely exapted for the regulation of human genes, particularly in breast cancer, where we have previously shown abundant cancer-specific transcription factor (TF) binding sites within the L1PA2 subfamily. In the current study, we performed a comprehensive analysis of TF binding activities in primate-specific L1 subfamilies and identified pervasive exaptation events amongst these evolutionarily related L1 transposons. By motif scanning, we predicted diverse and abundant TF binding potentials within the L1 transposons. We confirmed substantial TF binding activities in the L1 subfamilies using TF binding sites consolidated from an extensive collection of publicly available ChIP-seq datasets. Young L1 subfamilies (L1HS, L1PA2 and L1PA3) contributed abundant TF binding sites in MCF7 cells, primarily via their 5′ UTR. This is expected as the L1 5′ UTR hosts cis-regulatory elements that are crucial for L1 replication and mobilisation. Interestingly, the ancient L1 subfamilies, where 5′ truncation was common, displayed comparable TF binding capacity through their 3′ ends, suggesting an alternative exaptation mechanism in L1 transposons that was previously unnoticed. Overall, primate-specific L1 transposons were extensively exapted for TF binding in MCF7 breast cancer cells and are likely prominent genetic players modulating breast cancer transcriptional regulation.

Chromatin Immunoprecipitation followed by high-content sequencing (ChIP-seq) is a powerful approach for identifying bonafide transcription factor (TF) binding sites, however these studies can be difficult, time-consuming and costly. They require ChIP-validated antibodies and a priori knowledge of which TF to pull down. Moreover, to gain further mechanistic insights, transcriptomic data is required to determine if TF binding alters proximal gene expression. Determining regulatory pathways from expression data is not an easy task. The typical result of a gene expression experiment, a set of co-regulated genes, may not include the relevant TF at all. Many such factors become active through mechanisms other than a change in their level of expression. To understand how such a set of genes is co-regulated, it is necessary to find evidence of shared TF binding sites (TFBS). This approach presents its own set of problems. TFBS are identified by short sequences that can exist by chance without being biologically functional. An evolutionary perspective is required to consider only those functionally important sites that are conserved between the promoters of the genes in question and those of their orthologs in related species. Furthermore, the common occurrence of short TFBS makes it necessary to consider only those TFs that are significantly more common in the co-regulated genes than in the genome (or microarray) as a whole. InSilico-ChIP is a precomputed database of evolutionarily conserved TFBS for various species, which accepts a set of genes and quickly returns the conserved TFs that are statistically over-represented in the proximal promoter regions of those genes. It allows new species data to be created using only a whole genome alignment with a related species and gene locations. Several methods of identifying TF binding sites can be used, varying in alignment type, location, conservation restrictions, and TFBS matrices used to analyze the promoter regions.

Advances in high throughput sequencing technology have enabled the identification of transcription factor (TF) binding sites in genome scale. TF binding studies are important for medical applications and stem cell research. Somatic cells can be reprogrammed to a pluripotent state by the combined introduction of factors such as Oct4, Sox2, c-Myc, Klf4. These reprogrammed cells share many characteristics with embryonic stem cells (ESCs) and are known as induced pluripotent stem cells (iPSCs). The signaling requirements for maintenance of human and murine embryonic stem cells (ESCs) differ considerably. Genome wide ChIP-seq TF binding maps in mouse stem cells include Oct4, Sox2, Nanog, Tbx3, Smad2 as well as group of other factors. ChIP-seq allows study of new candidate transcription factors for reprogramming. It was shown that Nr5a2 could replace Oct4 for reprogramming. Epigenetic modifications play important role in regulation of gene expression adding additional complexity to transcription network functioning. We have studied associations between different histone modification using published data together with RNA Pol II sites. We found strong associations between activation marks and TF binding sites and present it qualitatively. To meet issues of statistical analysis of genome ChIP-sequencing maps we developed computer program to filter out noise signals and find significant association between binding site affinity and number of sequence reads. The data provide new insights into the function of chromatin organization and regulation in stem cells.

Molecular dissection of cis-regulatory modules at the Drosophila bithorax complex reveals critical transcription factor signature motifs

Elucidating the transcriptional regulatory networks that underlie growth and development requires robust ways to define the complete set of transcription factor (TF) binding sites. Although TF-binding sites are known to be generally located within accessible chromatin regions (ACRs), pinpointing these DNA regulatory elements globally remains challenging. Current approaches primarily identify binding sites for a single TF (e.g. ChIP-seq), or globally detect ACRs but lack the resolution to consistently define TF-binding sites (e.g. DNAse-seq, ATAC-seq). To address this challenge, we developed MNase-defined cistrome-Occupancy Analysis (MOA-seq), a high-resolution (< 30 bp), high-throughput, and genome-wide strategy to globally identify putative TF-binding sites within ACRs. We used MOA-seq on developing maize ears as a proof of concept, able to define a cistrome of 145,000 MOA footprints (MFs). While a substantial majority (76%) of the known ATAC-seq ACRs intersected with the MFs, only a minority of MFs overlapped with the ATAC peaks, indicating that the majority of MFs were novel and not detected by ATAC-seq. MFs were associated with promoters and significantly enriched for TF-binding and long-range chromatin interaction sites, including for the well-characterized FASCIATED EAR4, KNOTTED1, and TEOSINTE BRANCHED1. Importantly, the MOA-seq strategy improved the spatial resolution of TF-binding prediction and allowed us to identify 215 motif families collectively distributed over more than 100,000 non-overlapping, putatively-occupied binding sites across the genome. Our study presents a simple, efficient, and high-resolution approach to identify putative TF footprints and binding motifs genome-wide, to ultimately define a native cistrome atlas.

Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.

Genome-wide association studies (GWAS) have discovered thousands loci associated with disease risk and quantitative traits, yet most of the variants responsible for risk remain uncharacterized. The majority of GWAS-identified loci are enriched for non-coding single-nucleotide polymorphisms (SNPs) and defining the molecular mechanism of risk is challenging. Many non-coding causal SNPs are hypothesized to alter transcription factor (TF) binding sites as the mechanism by which they affect organismal phenotypes. We employed an integrative genomics approach to identify candidate TF binding motifs that confer breast cancer-specific phenotypes identified by GWAS. We performed de novo motif analysis of regulatory elements, analyzed evolutionary conservation of identified motifs, and assayed TF footprinting data to identify sequence elements that recruit TFs and maintain chromatin landscape in breast cancer-relevant tissue and cell lines. We identified candidate causal SNPs that are predicted to alter TF binding within breast cancer-relevant regulatory regions that are in strong linkage disequilibrium with significantly associated GWAS SNPs. We confirm that the TFs bind with predicted allele-specific preferences using CTCF ChIP-seq data. We used The Cancer Genome Atlas breast cancer patient data to identify ANKLE1 and ZNF404 as the target genes of candidate TF binding site SNPs in the 19p13.11 and 19q13.31 GWAS-identified loci. These SNPs are associated with the expression of ZNF404 and ANKLE1 in breast tissue. This integrative analysis pipeline is a general framework to identify candidate causal variants within regulatory regions and TF binding sites that confer phenotypic variation and disease risk.

Understanding transcriptional gene regulation is an important step to understanding how essential mechanisms are controlled in biological systems. Functional assayssuch as ChIP-seq and DNase I have been used to obtain a binding map of transcription factor (TF) binding sites on DNA and to determine the transcriptional regulatory network of TFs and their target genes. However, binding alone may notresult in a change in target gene expression. Experimental approaches to identifying functional binding events involve performing artificial TF knockdown experimentsor genome editing [31, 45, 70] and then declaring the differentially expressed genes as functionally validated target genes. Instead of artificial perturbation, in order to functionally validate the TF binding map, we propose to leverage the naturally occurring genetic variations as the source of perturbations that vary gene expressions and to analyze population single nucleotide polymorphism (SNP) and gene expression data. Experimental approaches typically target either a single TF or a family of TFs. In addition, in a single experiment, you must choose whether to perturb TF concentration through RNA interference or CRISPR interference, or TF binding affinity through genome editing. However, our approach is potentially more powerfulbecause any aspects of the TF-target interaction, including TF concentration and TF binding affinity, can be perturbed by a large number of SNPs found across the genome simultaneously and the effects are learned in a single analysis. In this thesis, we first introduce a statistical approach, based on conditional Gaussian Bayesian networks, that integrates population SNP and gene expression data with TF binding data to validate the TF binding map. We developed an efficientlearning algorithm for learning the gene regulatory network by using TF binding data as prior knowledge, and selecting the TF-target interactions that are validated based on population SNP and gene-expression data. Given the estimated network, we perform inference on the estimated probabilistic graphical models to determine downstream genes that are differentially expressed due to the effect of the TF-target interactions. We apply our method to learn transcriptional regulatory networks in lymphoblastoidcell lines (LCLs) and breast cancer tumours. First, we demonstrate our approach for validation of the TF binding map derived from ENCODE DNase I and ChIPseqdata from 71 TFs in LCLs, with SNP and gene expression data from the 1000 genomes and HapMap 3 projects respectively. We examined functional target genesthat were validated under perturbation of TF concentration and TF binding affinity. Finally, we apply our method to perform TF binding map validation for ER and itscoregulators which include 38 TFs obtained from Cistrome TF binding data, by using The Cancer Genome Atlas SNP and expression data from breast cancer tumors.We identified many previously known interactions between ER and its coregulators. We also found expression quantitative trait loci (eQTLs) in local binding regions oftarget genes that are potential super enhancers and eQTLs in coding regions that may affect the protein structure of important regulators.

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results:The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML). Methods: Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed in silico to examine their impact on TFBS clustering and predict changes in gene regulation. Results: The glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, was selected to test this approach. SLC25A32 and TANK exhibited the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required at least 1 information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.

Existing methods for computational prediction of transcription factor (TF) binding sites evaluate genomic regions with similarity to known TF sequence preferences. Most TF binding sites, however, do not resemble known TF sequence motifs, and many TFs are not sequence-specific. We developed Virtual ChIP-seq, which predicts binding of individual TFs in new cell types, integrating learned associations with gene expression and binding, TF binding sites from other cell types, and chromatin accessibility data in the new cell type. This approach outperforms methods that predict TF binding solely based on sequence preference, predicting binding for 36 TFs (MCC>0.3).

Cis-regulatory elements (CRE) are critical for coordinating gene expression programs that dictate cell-specific differentiation and homeostasis. Recently developed self-transcribing active regulatory region sequencing (STARR-Seq) has allowed for genome-wide annotation of functional CREs. Despite this, STARR-Seq assays are only employed in cell lines, in part, due to difficulties in delivering reporter constructs. Herein, we implemented and validated a STARR-Seq–based screen in human CD4+ T cells using a non-integrating lentiviral transduction system. Lenti-STARR-Seq is the first example of a genome-wide assay of CRE function in human primary cells, identifying thousands of functional enhancers and negative regulatory elements (NREs) in human CD4+ T cells. We find an unexpected difference in nucleosome organization between enhancers and NRE: enhancers are located between nucleosomes, whereas NRE are occupied by nucleosomes in their endogenous locations. We also describe chromatin modification, eRNA production, and transcription factor binding at both enhancers and NREs. Our findings support the idea of silencer repurposing as enhancers in alternate cell types. Collectively, these data suggest that Lenti-STARR-Seq is a successful approach for CRE screening in primary human cell types, and provides an atlas of functional CREs in human CD4+ T cells.