Cell Type-specific Binding Research Articles

LHX1-a multifunctional regulator in preoptic area-derived interneuron development.

LHX1-a multifunctional regulator in preoptic area-derived interneuron development.

Targeted DamID reveals differential binding of mammalian pluripotency factors.

The precise control of gene expression by transcription factor networks is crucial to organismal development. The predominant approach for mapping transcription factor-chromatin interactions has been chromatin immunoprecipitation (ChIP). However, ChIP requires a large number of homogeneous cells and antisera with high specificity. A second approach, DamID, has the drawback that high levels of Dam methylase are toxic. Here, we modify our targeted DamID approach (TaDa) to enable cell type-specific expression in mammalian systems, generating an inducible system (mammalian TaDa or MaTaDa) to identify genome-wide protein/DNA interactions in 100 to 1000 times fewer cells than ChIP-based approaches. We mapped the binding sites of two key pluripotency factors, OCT4 and PRDM14, in mouse embryonic stem cells, epiblast-like cells and primordial germ cell-like cells (PGCLCs). PGCLCs are an important system for elucidating primordial germ cell development in mice. We monitored PRDM14 binding during the specification of PGCLCs, identifying direct targets of PRDM14 that are key to understanding its crucial role in PGCLC development. We show that MaTaDa is a sensitive and accurate method for assessing cell type-specific transcription factor binding in limited numbers of cells.

Predicting transcription factor binding using ensemble random forest models.

Background: Understanding the location and cell-type specific binding of Transcription Factors (TFs) is important in the study of gene regulation. Computational prediction of TF binding sites is challenging, because TFs often bind only to short DNA motifs and cell-type specific co-factors may work together with the same TF to determine binding. Here, we consider the problem of learning a general model for the prediction of TF binding using DNase1-seq data and TF motif description in form of position specific energy matrices (PSEMs). Methods: We use TF ChIP-seq data as a gold-standard for model training and evaluation. Our contribution is a novel ensemble learning approach using random forest classifiers. In the context of the ENCODE-DREAM in vivo TF binding site prediction challenge we consider different learning setups. Results: Our results indicate that the ensemble learning approach is able to better generalize across tissues and cell-types compared to individual tissue-specific classifiers or a classifier applied to the data aggregated across tissues. Furthermore, we show that incorporating DNase1-seq peaks is essential to reduce the false positive rate of TF binding predictions compared to considering the raw DNase1 signal. Conclusions: Analysis of important features reveals that the models preferentially select motifs of other TFs that are close interaction partners in existing protein protein-interaction networks. Code generated in the scope of this project is available on GitHub: https://github.com/SchulzLab/TFAnalysis (DOI: 10.5281/zenodo.1409697).

The Sub-Chromosomic Macronuclear Pheromone Genes of the Ciliate Euplotes raikovi: Comparative Structural Analysis and Insights into the Mechanism of Expression.

In Euplotes raikovi, we have determined the full-length sequences of a family of macronuclear genes that are the transcriptionally active versions of codominant alleles inherited at the mating-type (mat) locus of the micronuclear genome, and encode cell type-distinctive signaling pheromones. These genes include a 225-231-bp coding region flanked by a conserved 544-bp 5'-leader region and a more variable 3'-trailer region. Two transcription initiation start sites and two polyadenylation sites associated with nonconventional signals cooperate with a splicing phenomenon of a 326-bp intron residing in the 5'-leader region in the generation of multiple transcripts from the same gene. In two of them, the synthesis of functional products depends on the reassignment to a sense codon, or readthrough of a strictly conserved leaky UAG stop codon. That this reassignment may take place is suggested by the position this codon occupies in the transcripts, close to the transcript extremity and far from the poly(A) tail. In such a case, one product is a 69-amino acid protein in search of function and the second product is a 126-amino acid protein that represents a membrane-bound pheromone isoform candidate to function as a cell type-specific binding site (receptor) of the soluble pheromones.

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data. The changes in the TF binding intensities between the altered sequence and the reference sequence reflect the degree of functional impact for the variant. This enables us to develop the tool DeFine (Deep learning based Functional impact of non-coding variants evaluator, http://define.cbi.pku.edu.cn) with improved performance for assessing the functional impact of non-coding variants including SNPs and indels. DeFine accurately identifies the causal functional non-coding variants from disease-associated variants in GWAS. DeFine is an effective and easy-to-use tool that facilities systematic prioritization of functional non-coding variants.

Heparan Sulfate Proteoglycans as Emerging Players in Synaptic Specificity.

Neural circuits consist of distinct neuronal cell types connected in specific patterns. The specificity of these connections is achieved in a series of sequential developmental steps that involve the targeting of neurites, the identification of synaptic partners, and the formation of specific types of synapses. Cell-surface proteins play a critical role in each of these steps. The heparan sulfate proteoglycan (HSPG) family of cell-surface proteins is emerging as a key regulator of connectivity. HSPGs are expressed throughout brain development and play important roles in axon guidance, synapse development and synapse function. New insights indicate that neuronal cell types express unique combinations of HSPGs and HS-modifying enzymes. Furthermore, HSPGs interact with cell type-specific binding partners to mediate synapse development. This suggests that cell type-specific repertoires of HSPGs and specific patterns of HS modifications on the cell surface are required for the development of specific synaptic connections. Genome-wide association studies have linked these proteins to neurodevelopmental and neuropsychiatric diseases. Thus, HSPGs play an important role in the development of specific synaptic connectivity patterns important for neural circuit function, and their dysfunction may be involved in the development of brain disorders.

STAT3 is required for proliferation and exhibits a cell type-specific binding preference in mouse female germline stem cells.

LIF-mediated STAT3 signaling is critically involved in stem cells and development. However, its function in mouse female germline cells (FGSCs) remains elusive. In this study, we demonstrated that LIF-induced STAT3 activation contributes to the proliferation and undifferentiation maintenance of mouse FGSCs. Characterization of the STAT3-mediated transcriptional network by intersecting ChIP-seq and RNA-seq datasets revealed 405 direct target genes of STAT3, which are primarily involved in proliferation and germline development. In particular, we observed that STAT3 exhibits a FGSC-specific binding pattern when compared with mouse embryonic stem cells. Taken together, our study reported that the LIF-mediated STAT3 activation is actively involved in FGSCs and functions through a distinctive binding pattern across the FGSC genome. This cell-type specific binding preference provides an insight into understanding the genetic base for STAT3-driven cellular functions in germline stem cells.

Role of the chromatin landscape and sequence in determining cell type-specific genomic glucocorticoid receptor binding and gene regulation

Role of the chromatin landscape and sequence in determining cell type-specific genomic glucocorticoid receptor binding and gene regulation

Role of the chromatin landscape and sequence in determining cell type-specific genomic glucocorticoid receptor binding and gene regulation.

The genomic loci bound by the glucocorticoid receptor (GR), a hormone-activated transcription factor, show little overlap between cell types. To study the role of chromatin and sequence in specifying where GR binds, we used Bayesian modeling within the universe of accessible chromatin. Taken together, our results uncovered that although GR preferentially binds accessible chromatin, its binding is biased against accessible chromatin located at promoter regions. This bias can only be explained partially by the presence of fewer GR recognition sequences, arguing for the existence of additional mechanisms that interfere with GR binding at promoters. Therefore, we tested the role of H3K9ac, the chromatin feature with the strongest negative association with GR binding, but found that this correlation does not reflect a causative link. Finally, we find a higher percentage of promoter–proximal GR binding for genes regulated by GR across cell types than for cell type-specific target genes. Given that GR almost exclusively binds accessible chromatin, we propose that cell type-specific regulation by GR preferentially occurs via distal enhancers, whose chromatin accessibility is typically cell type-specific, whereas ubiquitous target gene regulation is more likely to result from binding to promoter regions, which are often accessible regardless of cell type examined.

Two-Step Regulation of a Meristematic Cell Population Acting in Shoot Branching in Arabidopsis.

Shoot branching requires the establishment of new meristems harboring stem cells; this phenomenon raises questions about the precise regulation of meristematic fate. In seed plants, these new meristems initiate in leaf axils to enable lateral shoot branching. Using live-cell imaging of leaf axil cells, we show that the initiation of axillary meristems requires a meristematic cell population continuously expressing the meristem marker SHOOT MERISTEMLESS (STM). The maintenance of STM expression depends on the leaf axil auxin minimum. Ectopic expression of STM is insufficient to activate axillary buds formation from plants that have lost leaf axil STM expressing cells. This suggests that some cells undergo irreversible commitment to a developmental fate. In more mature leaves, REVOLUTA (REV) directly up-regulates STM expression in leaf axil meristematic cells, but not in differentiated cells, to establish axillary meristems. Cell type-specific binding of REV to the STM region correlates with epigenetic modifications. Our data favor a threshold model for axillary meristem initiation, in which low levels of STM maintain meristematic competence and high levels of STM lead to meristem initiation.

Heterogeneity of transcription factor binding specificity models within and across cell lines.

Complex gene expression patterns are mediated by the binding of transcription factors (TFs) to specific genomic loci. The in vivo occupancy of a TF is, in large part, determined by the TF's DNA binding interaction partners, motivating genomic context-based models of TF occupancy. However, approaches thus far have assumed a uniform TF binding model to explain genome-wide cell-type–specific binding sites. Therefore, the cell type heterogeneity of TF occupancy models, as well as the extent to which binding rules underlying a TF's occupancy are shared across cell types, has not been investigated. Here, we develop an ensemble-based approach (TRISECT) to identify the heterogeneous binding rules for cell-type–specific TF occupancy and analyze the inter-cell-type sharing of such rules. Comprehensive analysis of 23 TFs, each with ChIP-seq data in four to 12 different cell types, shows that by explicitly capturing the heterogeneity of binding rules, TRISECT accurately identifies in vivo TF occupancy. Importantly, many of the binding rules derived from individual cell types are shared across cell types and reveal distinct yet functionally coherent putative target genes in different cell types. Closer inspection of the predicted cell-type–specific interaction partners provides insights into the context-specific functional landscape of a TF. Together, our novel ensemble-based approach reveals, for the first time, a widespread heterogeneity of binding rules, comprising the interaction partners within a cell type, many of which nevertheless transcend cell types. Notably, the putative targets of shared binding rules in different cell types, while distinct, exhibit significant functional coherence.

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BackgroundUnderstanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process.MethodsOur study primarily relies on recent data published by the ENCODE consortium. Five dissimilar TFs assayed in multiple cell-types were selected as examples: CTCF, JunD, REST, GABP and USF2. We used two types of candidate target sites: (a) predicted sites obtained by scanning the whole genome with a position weight matrix, and (b) cell-type specific peak lists provided by ENCODE. Quantitative in vivo occupancy levels in different cell-types were based on ChIP-seq data for the corresponding TFs. In parallel, we computed a number of associated sequence-intrinsic and experimental features (histone modification, DNase I hypersensitivity, etc.) for each site. Machine learning algorithms were then used in a binary classification and regression framework to predict site occupancy and binding strength, for the purpose of assessing the relative importance of different contextual features.ResultsWe observed striking differences in the feature importance rankings between the five factors tested. PWM-scores were amongst the most important features only for CTCF and REST but of little value for JunD and USF2. Chromatin accessibility and active histone marks are potent predictors for all factors except REST. Structural DNA parameters, repressive and gene body associated histone marks are generally of little or no predictive value.ConclusionsWe define a general and extensible computational framework for analyzing the importance of various DNA-intrinsic and chromatin-associated features in determining cell-type specific TF binding to target sites. The application of our methodology to ENCODE data has led to new insights on transcription regulatory processes and may serve as example for future studies encompassing even larger datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0846-z) contains supplementary material, which is available to authorized users.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.

BackgroundTranscription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.ResultsWe present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity.ConclusionsOur results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0834-3) contains supplementary material, which is available to authorized users.

Genome-wide identification of hypoxia-inducible factor-1 and -2 binding sites in hypoxic human macrophages alternatively activated by IL-10

Macrophages (MΦ) often accumulate in hypoxic areas, where they significantly influence disease progression. Anti-inflammatory cytokines, such as IL-10, generate alternatively activated macrophages that support tumor growth. To understand how alternative activation affects the transcriptional profile of hypoxic macrophages, we globally mapped binding sites of hypoxia-inducible factor (HIF)-1α and HIF-2α in primary human monocyte-derived macrophages prestimulated with IL-10. 713 HIF-1 and 795 HIF-2 binding sites were identified under hypoxia. Pretreatment with IL-10 altered the binding pattern, with 120 new HIF-1 and 188 new HIF-2 binding sites emerging. HIF-1 binding was most prominent in promoters, while HIF-2 binding was more abundant in enhancer regions. Comparison of ChIP-seq data obtained in other cells revealed a highly cell type specific binding of HIF. In MΦ HIF binding occurred preferentially in already active enhancers or promoters. To assess the roles of HIF on gene expression, primary human macrophages were treated with siRNA against HIF-1α or HIF-2α, followed by genome-wide gene expression analysis. Comparing mRNA expression to the HIF binding profile revealed a significant enrichment of hypoxia-inducible genes previously identified by ChIP-seq. Analysis of gene expression under hypoxia alone and hypoxia/IL-10 showed the enhanced induction of a set of genes including PLOD2 and SLC2A3, while another group including KDM3A and ADM remained unaffected or was reduced by IL-10. Taken together IL-10 influences the DNA binding pattern of HIF and the level of gene induction

Shared transcription factors contribute to distinct cell fates

Genome-wide transcription factor (TF) binding profiles differ dramatically between cell types. However, not much is known about the relationship between cell-type-specific binding patterns and gene expression. A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells. Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

Key regulators control distinct transcriptional programmes in blood progenitor and mast cells.

Despite major advances in the generation of genome-wide binding maps, the mechanisms by which transcription factors (TFs) regulate cell type identity have remained largely obscure. Through comparative analysis of 10 key haematopoietic TFs in both mast cells and blood progenitors, we demonstrate that the largely cell type-specific binding profiles are not opportunistic, but instead contribute to cell type-specific transcriptional control, because (i) mathematical modelling of differential binding of shared TFs can explain differential gene expression, (ii) consensus binding sites are important for cell type-specific binding and (iii) knock-down of blood stem cell regulators in mast cells reveals mast cell-specific genes as direct targets. Finally, we show that the known mast cell regulators Mitf and c-fos likely contribute to the global reorganisation of TF binding profiles. Taken together therefore, our study elucidates how key regulatory TFs contribute to transcriptional programmes in several distinct mammalian cell types.

Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments

Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species.

Genome scale transcriptional control of haematopoietic cell type identity

Transcription factors (TFs) have long been recognised as powerful regulators of cell type identity. The underlying mechanisms however have remained largely obscure, despite major advances in the generation of genome-wide binding maps. Moreover, how so-called master regulators can contribute to the functionality of multiple distinct cell types is even less-well understood. Here we have compared global binding patterns for 10 key haematopoietic TFs in both mast cells and blood progenitors, revealing largely cell-type specific binding profiles.

Synergistic binding of transcription factors to cell-specific enhancers programs motor neuron identity

Efficient transcriptional programming promises to open new frontiers in regenerative medicine. However, mechanisms by which programming factors transform cell fate are unknown, preventing more rational selection of factors to generate desirable cell types. Three transcription factors, Ngn2, Isl1 and Lhx3, were sufficient to program rapidly and efficiently spinal motor neuron identity when expressed in differentiating mouse embryonic stem cells. Replacement of Lhx3 by Phox2a led to specification of cranial, rather than spinal, motor neurons. Chromatin immunoprecipitation-sequencing analysis of Isl1, Lhx3 and Phox2a binding sites revealed that the two cell fates were programmed by the recruitment of Isl1-Lhx3 and Isl1-Phox2a complexes to distinct genomic locations characterized by a unique grammar of homeodomain binding motifs. Our findings suggest that synergistic interactions among transcription factors determine the specificity of their recruitment to cell type-specific binding sites and illustrate how a single transcription factor can be repurposed to program different cell types.

Different GATA Factors Dictate CCR3 Transcription in Allergic Inflammatory Cells in a Cell Type–Specific Manner

The chemokine receptor CCR3 is expressed in prominent allergic inflammatory cells, including eosinophils, mast cells, and Th2 cells. We previously identified a functional GATA element within exon 1 of the CCR3 gene that is responsible for GATA-1-mediated CCR3 transcription. Because allergic inflammatory cells exhibit distinct expression patterns of different GATA factors, we investigated whether different GATA factors dictate CCR3 transcription in a cell type-specific manner. GATA-2 was expressed in EoL-1 eosinophilic cells, GATA-1 and GATA-2 were expressed in HMC-1 mast cells, and GATA-3 was preferentially expressed in Jurkat cells. Unlike a wild-type CCR3 reporter, reporters lacking the functional GATA element were not active in any of the three cell types, implying the involvement of different GATA factors in CCR3 transcription. RNA interference assays showed that small interfering RNAs specific for different GATA factors reduced CCR3 reporter activity in a cell type-specific fashion. Consistent with these findings, chromatin immunoprecipitation and EMSA analyses demonstrated cell type-specific binding of GATA factors to the functional GATA site. More importantly, specific inhibition of the CCR3 reporter activity by different GATA small interfering RNAs was well preserved in respective cell types differentiated from cord blood; in particular, GATA-3 was entirely responsible for reporter activity in Th2 cells and replaced the role predominantly played by GATA-1 and GATA-2. These results highlight a mechanistic role of GATA factors in which cell type-specific expression is the primary determinant of transcription of the CCR3 gene in major allergic inflammatory cells.