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Cell type identity is encoded by gene regulatory networks (GRN), in which transcription factors (TFs) bind to enhancers to regulate target gene expression. In the mammalian liver, lineage TFs have been characterized for the main cell types, including hepatocytes. Hepatocytes cover a relatively broad cellular state space, as they differ significantly in their metabolic state, and function, depending on their position with respect to the central or portal vein in a liver lobule. It is unclear whether this spatially defined cellular state space, called zonation, is also governed by a well-defined gene regulatory code. To address this challenge, we have mapped enhancer-GRNs across liver cell types at high resolution, using a combination of single cell multiomics, spatial omics, GRN inference, and deep learning. We found that cell state changes in transcription and chromatin accessibility in hepatocytes, liver sinusoidal endothelial cells and hepatic stellate cells depend on zonation. Enhancer-GRN mapping suggests that zonation states in hepatocytes are driven by the repressors Tcf7l1 and Tbx3, that modulate the core hepatocyte GRN, controlled by Hnf4a, Cebpa, Hnf1a, Onecut1 and Foxa1, among others. To investigate how these TFs cooperate with cell type TFs, we performed anin vivomassively parallel reporter assay on 12,000 hepatocyte enhancers and used these data to train a hierarchical deep learning model (called DeepLiver) that exploits both enhancer accessibility and activity. DeepLiver confirms Cebpa, Onecut, Foxa1, Hnf1a and Hnf4a as drivers of enhancer specificity in hepatocytes; Tcf7l1/2 and Tbx3 as regulators of the zonation state; and Hnf4a, Hnf1a, AP-1 and Ets as activators. Finally, taking advantage ofin silicomutagenesis predictions from DeepLiver and enhancer assays, we confirmed that the destruction of Tcf7l1/2 or Tbx3 motifs in zonated enhancers abrogates their zonation bias. Our study provides a multi-modal understanding of the regulatory code underlying hepatocyte identity and their zonation state, that can be exploited to engineer enhancers with specific activity levels and zonation patterns.

Joint profiling of chromatin accessibility and gene expression of individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (eGRN). Here we present a new method for the inference of eGRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TF) and links these enhancers to candidate target genes. Specific TFs for each cell type or cell state are predicted based on the concordance of TF binding site accessibility, TF expression, and target gene expression. To improve both recall and precision of TF identification, we curated and clustered more than 40,000 position weight matrices that we could associate with 1,553 human TFs. We validated and benchmarked each of the SCENIC+ components on diverse data sets from different species, including human peripheral blood mononuclear cell types, ENCODE cell lines, human melanoma cell states, and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers, and GRNs between human and mouse cell types in the cerebral cortex. Finally, we provide new capabilities that exploit the inferred eGRNs to study the dynamics of gene regulation along differentiation trajectories; to map regulatory activities onto tissues using spatial omics data; and to predict the effect of TF perturbations on cell state. SCENIC+ provides critical insight into gene regulation, starting from multiome atlases of scATAC-seq and scRNA-seq. The SCENIC+ suite is available as a set of Python modules at https://scenicplus.readthedocs.io.

Wound response programs are often activated during neoplastic growth in tumors. In both wound repair and tumor growth, cells respond to acute stress and balance the activation of multiple programs including apoptosis, proliferation, and cell migration. Central to those responses are the activation of the JNK/MAPK and JAK/STAT signaling pathways. Yet, to what extent these signaling cascades interact at the cis-regulatory level, and how they orchestrate different regulatory and phenotypic responses is still unclear. Here, we aim to characterize the regulatory states that emerge and cooperate in the wound response, using the Drosophila melanogaster wing disc as a model system, and compare these with cancer cell states induced by rasV12scrib-/- in the eye disc. We used single-cell multiome profiling to derive enhancer Gene Regulatory Networks (eGRNs) by integrating chromatin accessibility and gene expression signals. We identify a “proliferative” eGRN, active in the majority of wounded cells and controlled by AP-1 and STAT. In a smaller, but distinct population of wound cells, a “senescent” eGRN is activated and driven by C/EBP-like transcription factors (Irbp18, Xrp1, Slow border, and Vrille) and Scalloped. In tumor cells on the other hand, both eGRN signatures are simultaneously active within the same cell. Our single-cell multiome and eGRNs resource offers an in-depth characterisation of the senescence markers, together with a new perspective on the shared gene regulatory programs acting during wound response and oncogenesis.

Melanoma cells can switch between a melanocytic and a mesenchymal-like state. Scattered evidence indicates that additional intermediate state(s) may exist. Here, to search for such states and decipher their underlying gene regulatory network (GRN), we studied 10 melanoma cultures using single-cell RNA sequencing (RNA-seq) as well as 26 additional cultures using bulk RNA-seq. Although each culture exhibited a unique transcriptome, we identified shared GRNs that underlie the extreme melanocytic and mesenchymal states and the intermediate state. This intermediate state is corroborated by a distinct chromatin landscape and is governed by the transcription factors SOX6, NFATC2, EGR3, ELF1 and ETV4. Single-cell migration assays confirmed the intermediate migratory phenotype of this state. Using time-series sampling of single cells after knockdown of SOX10, we unravelled the sequential and recurrent arrangement of GRNs during phenotype switching. Taken together, these analyses indicate that an intermediate state exists and is driven by a distinct and stable 'mixed' GRN rather than being a symbiotic heterogeneous mix of cells. ispartof: NATURE CELL BIOLOGY vol:22 issue:8 pages:986-+ ispartof: location:England status: published

The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation. ispartof: NATURE vol:601 issue:7894 pages:630-+ ispartof: location:England status: published

SummaryTheDrosophilabrain is a work horse in neuroscience. Single-cell transcriptome analysis1–5,3D morphological classification6, and detailed EM mapping of the connectome7–10have revealed an immense diversity of neuronal and glial cell types that underlie the wide array of functional and behavioral traits in the fruit fly. The identities of these cell types are controlled by – still unknown – gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. To characterize the GRN for each cell type in theDrosophilabrain, we profiled chromatin accessibility of 240,919 single cells spanning nine developmental timepoints, and integrated this data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which around 70,000 are linked to specific developmental trajectories, involving neurogenesis, reprogramming and maturation. For 40 cell types, their uniquely accessible regions could be associated with their expressed transcription factors and downstream target genes, through a combination of motif discovery, network inference techniques, and deep learning. We illustrate how these “enhancer-GRNs” can be used to reveal enhancer architectures leading to a better understanding of neuronal regulatory diversity. Finally, our atlas of regulatory elements can be used to design genetic driver lines for specific cell types at specific timepoints, facilitating the characterization of brain cell types and the manipulation of brain function.

Understanding how enhancers drive cell type specificity and efficiently identifying them is essential for the development of innovative therapeutic strategies. In melanoma, the melanocytic (MEL) and the mesenchymal-like (MES) states present themselves with different responses to therapy, making the identification of specific enhancers highly relevant. Using massively parallel reporter assay (MPRA) in a panel of patient-derived melanoma lines (MM lines), we set to identify and decipher melanoma enhancers by first focusing on regions with state specific H3K27 acetylation close to differentially expressed genes. A more in-depth evaluation of those regions was then pursued by investigating the activity of ATAC-seq peaks found therein along with a full tiling of the acetylated regions with 190 bp sequences. Activity was observed in more than 60% of the selected regions and we were able to precisely locate the active regions within ATAC-seq peaks. Comparison of sequence content with activity, using the deep learning model DeepMEL2, revealed that AP-1 alone is responsible for the MES enhancer activity, while SOX and MITF both influence MEL enhancer activity with SOX being required to achieve high levels of activity. Overall, our MPRA assays shed light on the relationship between long and short sequences in terms of their sequence content, enhancer activity, and specificity as reporters across melanoma cell states.

We present cisTopic, a probabilistic framework used to simultaneously discover coaccessible enhancers and stable cell states from sparse single-cell epigenomics data ( http://github.com/aertslab/cistopic ). Using a compendium of single-cell ATAC-seq datasets from differentiating hematopoietic cells, brain and transcription factor perturbations, we demonstrate that topic modeling can be exploited for robust identification of cell types, enhancers and relevant transcription factors. cisTopic provides insight into the mechanisms underlying regulatory heterogeneity in cell populations. As an unsupervised Bayesian framework, cisTopic classifies regions in scATAC-seq data into regulatory topics, which are used for clustering.

Single-cell RNA-seq and single-cell ATAC-seq technologies are being used extensively to create cell type atlases for a wide range of organisms, tissues, and disease processes. To increase the scale of these atlases, lower the cost, and allow for more specialized multi-ome assays, custom droplet microfluidics may provide complementary solutions to commercial setups. We developed HyDrop, a flexible and generic droplet microfluidic platform encompassing three protocols. The first protocol involves creating dissolvable hydrogel beads with custom oligos that can be released in the droplets. In the second protocol, we demonstrate the use of these beads for HyDrop-ATAC, a low-cost non-commercial scATAC-seq protocol in droplets. After validating HyDrop-ATAC, we applied it to flash-frozen mouse cortex and generated 8,502 high-quality single-cell chromatin accessibility profiles in a single run. In the third protocol, we adapt both the reaction chemistry and the capture sequence of the barcoded hydrogel bead to capture mRNA, and demonstrate a significant improvement in throughput and sensitivity compared to previous open-source droplet-based scRNA-seq assays (Drop-seq and inDrop). Similarly, we applied HyDrop-RNA to flash-frozen mouse cortex and generated 9,508 single-cell transcriptomes closely matching reference single-cell gene expression data. Finally, we leveraged HyDrop-RNA’s high capture rate to analyse a small population of FAC-sorted neurons from the Drosophila brain, confirming the protocol’s applicability to low-input samples and small cells. HyDrop is currently capable of generating single-cell data in high throughput and at a reduced cost compared to commercial methods, and we envision that HyDrop can be further developed to be compatible with novel (multi-) omics protocols.

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%–20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression., SCOPUS: ar.j, info:eu-repo/semantics/published

Deciphering the genomic regulatory code of enhancers is a key challenge in biology because this code underlies cellular identity. A better understanding of how enhancers work will improve the interpretation of noncoding genome variation and empower the generation of cell type-specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable enhancer models. We apply this strategy to decipher the enhancer code in melanoma, a relevant case study owing to the presence of distinct melanoma cell states. We trained and validated a deep learning model, called DeepMEL, using chromatin accessibility data of 26 melanoma samples across six different species. We show the accuracy of DeepMEL predictions on the CAGI5 challenge, where it significantly outperforms existing models on the melanoma enhancer of IRF4 Next, we exploit DeepMEL to analyze enhancer architectures and identify accurate transcription factor binding sites for the core regulatory complexes in the two different melanoma states, with distinct roles for each transcription factor, in terms of nucleosome displacement or enhancer activation. Finally, DeepMEL identifies orthologous enhancers across distantly related species, where sequence alignment fails, and the model highlights specific nucleotide substitutions that underlie enhancer turnover. DeepMEL can be used from the Kipoi database to predict and optimize candidate enhancers and to prioritize enhancer mutations. In addition, our computational strategy can be applied to other cancer or normal cell types. ispartof: GENOME RESEARCH vol:30 issue:12 ispartof: location:United States status: published

Transcription factors regulate their target genes by binding to regulatory regions in the genome. Although the binding preferences of TP53 are known, it remains unclear what distinguishes functional enhancers from nonfunctional binding. In addition, the genome is scattered with recognition sequences that remain unoccupied. Using two complementary techniques of multiplex enhancer-reporter assays, we discovered that functional enhancers could be discriminated from nonfunctional binding events by the occurrence of a single TP53 canonical motif. By combining machine learning with a meta-analysis of TP53 ChIP-seq data sets, we identified a core set of more than 1000 responsive enhancers in the human genome. This TP53 cistrome is invariably used between cell types and experimental conditions, whereas differences among experiments can be attributed to indirect nonfunctional binding events. Our data suggest that TP53 enhancers represent a class of unsophisticated cell-autonomous enhancers containing a single TP53 binding site, distinct from complex developmental enhancers that integrate signals from multiple transcription factors. ispartof: Genome Research vol:26 issue:7 pages:882-895 ispartof: location:United States status: published

Summary The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain., Graphical Abstract, Highlights • A single-cell atlas of the adult fly brain during aging • Network inference reveals regulatory states related to oxidative phosphorylation • Cell identity is retained during aging despite exponential decline of gene expression • SCope: An online tool to explore and compare single-cell datasets across species, A single-cell atlas of adult fly brains identifies the ensemble of neuronal and glial cell types and their dynamic changes during aging.

Single-cell epigenomics provides new opportunities to decipher genomic regulatory programs from heterogeneous samples and dynamic processes. We present a probabilistic framework called cisTopic, to simultaneously discover “cis-regulatory topics” and stable cell states from sparse single-cell epigenomics data. After benchmarking cisTopic on single-cell ATAC-seq data, single-cell DNA methylation data, and semi-simulated single-cell ChIP-seq data, we use cisTopic to predict regulatory programs in the human brain and validate these by aligning them with co-expression networks derived from single-cell RNA-seq data. Next, we performed a time-series single-cell ATAC-seq experiment after SOX10 perturbations in melanoma cultures, where cisTopic revealed dynamic regulatory topics driven by SOX10 and AP-1. Finally, machine learning and enhancer modelling approaches allowed to predict cell type specific SOX10 and SOX9 binding sites based on topic specific co-regulatory motifs. cisTopic is available as an R/Bioconductor package at http://github.com/aertslab/cistopic.

Transcriptional enhancers function as docking platforms for combinations of transcription factors (TFs) to control gene expression. How enhancer sequences determine nucleosome occupancy, TF recruitment and transcriptional activation in vivo remains unclear. Using ATAC–seq across a panel of Drosophila inbred strains, we found that SNPs affecting binding sites of the TF Grainy head (Grh) causally determine the accessibility of epithelial enhancers. We show that deletion and ectopic expression of Grh cause loss and gain of DNA accessibility, respectively. However, although Grh binding is necessary for enhancer accessibility, it is insufficient to activate enhancers. Finally, we show that human Grh homologs—GRHL1, GRHL2 and GRHL3—function similarly. We conclude that Grh binding is necessary and sufficient for the opening of epithelial enhancers but not for their activation. Our data support a model positing that complex spatiotemporal expression patterns are controlled by regulatory hierarchies in which pioneer factors, such as Grh, establish tissue-specific accessible chromatin landscapes upon which other factors can act. ispartof: NATURE GENETICS vol:50 issue:7 pages:1011-1020 ispartof: location:United States status: published

The identification of functional non-coding mutations is a key challenge in the field of genomics, where whole-genome re-sequencing can swiftly generate a set of all genomic variants in a sample, such as a tumor biopsy. The size of the human regulatory landscape places a challenge on finding recurrent cis-regulatory mutations across samples of the same cancer type. Therefore, powerful computational approaches are required to sift through the tens of thousands of non-coding variants, to identify potentially functional variants that have an impact on the gene expression profile of the sample. Here we introduce an integrative analysis pipeline, called μ- cisTarget, to filter, annotate and prioritize non-coding variants based on their putative effect on the underlying 'personal' gene regulatory network. We first validate μ-cisTarget by re-analyzing three cases of oncogenic non-coding mutations, namely the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genome of ten cancer cell lines of six different cancer types, and used matched transcriptome data and motif discovery to infer master regulators for each sample. We identified candidate functional non-coding mutations that generate de novo binding sites for these master regulators, and that result in the up-regulation of nearby oncogenic drivers. We finally validated the predictions using tertiary data including matched epigenome data. Our approach is generally applicable to re-sequenced cancer genomes, or other genomes, when a disease- or sample-specific gene signature is available for network inference. μ-cisTarget is available from http://mucistarget.aertslab.org.

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRNs). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse engineer the GRNs of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types and inferred a coexpression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5,632 direct target genes through 24,926 enhancers. Using this network, we found network motifs, cis-regulatory codes, and regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general cofactor in the eye network, being bound to thousands of nucleosome-free regions. publisher: Elsevier articletitle: Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference journaltitle: Cell Reports articlelink: http://dx.doi.org/10.1016/j.celrep.2014.11.038 content_type: article copyright: Copyright © 2014 The Authors. Published by Elsevier Inc. ispartof: Cell Reports vol:9 issue:6 pages:2290-303 ispartof: location:United States status: published

Background CD36 is a membrane glycoprotein, contributing to the pathogenesis of metabolic disorders, like obesity, which has become a major health concern worldwide. Methods A potential functional role of the scavenger receptor CD36 was investigated in in vitro adipocyte differentiation and in vivo adipogenesis. Results During differentiation of 3T3-F442A preadipocytes into mature adipocytes, expression of CD36 was upregulated and CD36 gene silencing resulted in impaired differentiation, as monitored by Oil Red O staining and expression of adipogenic markers. De novo fat pad formation in NUDE mice following injection of preadipocytes was significantly reduced upon CD36 gene silencing as compared to control. This was associated with marked adipocyte hypotrophy and reduced adipose tissue adipocyte content. Macrophage infiltration in de novo fat tissues derived from preadipocytes with CD36 gene silencing was not significantly different from controls. Collagen content was significantly higher in de novo fat with CD36 gene silencing. In a nutritionally induced obesity model, total body weight as well as subcutaneous and gonadal adipose tissue mass were significantly lower in CD36 deficient mice as compared to wild-type littermates. General significance Thus, our data support a functional role of CD36 in promoting adipogenesis in vitro as well as in vivo.

Type 2 diabetes is causally related to obesity and characterized by dysfunctional pancreatic beta cells. It is so far unclear whether direct interactions exist between adipocytes and beta cells and possibly raise any pathogenic relevance. In this study, we examined whether 9-day co-cultured 3T3-F442A (pre)adipocytes and primary rat pancreatic beta cells exert an influence on each other's function. In the presence of beta cells, 3T3-F442A cells became lipid-storing cells expressing markers of differentiated adipocytes and releasing adiponectin. This effect was attributed to the medium insulin levels (around 0.1 μM) and was associated with an elevated glucose consumption by the 3T3-F442A cells. The subsequent decrease in medium glucose concentration reduced the rate of insulin release by beta cells cultured at 10 mM glucose, and thus suppressed their degranulation during culture. These changes in beta cell function did not occur at 20 mM glucose and were reversible upon removal of the 3T3-F422A cells. They could not be reproduced by 3T3-F422A-conditioned medium containing varying adiponectin concentrations. These data indicate that insulin secreted by beta cells is sufficient to induce differentiation of preadipocytes without addition of exogenous adipogenic factors. Over 9 days culture, (pre)adipocytes did not directly and irreversibly affect beta cell functions.

The potential role of placental growth factor (PlGF) in early stages of adipogenesis was investigated in vivo using a murine model of obesity, as well as in vitro using cultured preadipocytes. PlGF-deficient (PlGF-/-) and wild-type (WT) mice, kept on high fat diet (HFD) for 3 weeks, had comparable body weight and weight of subcutaneous (SC) and gonadal (GON) adipose tissues. Blood vessel size and blood vessel density, normalized to adipocyte number, were not significantly different in SC and GON adipose tissues of both genotypes. Differentiation of embryonic fibroblasts derived from WT or PlGF-/- mice into mature adipocytes was comparable. Furthermore, addition of recombinant PlGF, of the PlGF neutralizing MAb PL5D11D4 or of the anti-Flk-1 MAb DC101 to cultured 3T3-F442A preadipocytes did not significantly affect their differentiation into mature adipocytes. Ex vivo blood vessel outgrowth following seeding of adipose tissue-derived microvessel fragments in 3D-collagen gels was not affected by PlGF deficiency. Thus, in murine model systems, PlGF does not seem to play an important role in early adipogenesis.

Scoring the impact of noncoding variation on the function of cis-regulatory regions, on their chromatin state, and on the qualitative and quantitative expression levels of target genes is a fundamental problem in evolutionary genomics. A particular challenge is how to model the divergence of quantitative traits and to identify relationships between the changes across the different levels of the genome, the chromatin activity landscape, and the transcriptome. Here, we examine the use of the Ornstein-Uhlenbeck (OU) model to infer selection at the level of predicted cis-regulatory modules (CRMs), and link these with changes in transcription factor binding and chromatin activity. Using publicly available cross-species ChIP-Seq and STARR-Seq data we show how OU can be applied genome-wide to identify candidate transcription factors for which binding site and CRM turnover is correlated with changes in regulatory activity. Next, we profile open chromatin in the developing eye across three Drosophila species. We identify the recognition motifs of the chromatin remodelers, Trithorax-like and Grainyhead as mostly correlating with species-specific changes in open chromatin. In conclusion, we show in this study that CRM scores can be used as quantitative traits and that motif discovery approaches can be extended towards more complex models of divergence. ispartof: Molecular Biology and Evolution vol:32 issue:9 pages:2441-55 ispartof: location:United States status: published

Genomic enhancers regulate spatio-temporal gene expression by recruiting specific combinations of transcription factors (TFs). When TFs are bound to active regulatory regions, they displace canonical nucleosomes, making these regions biochemically detectable as nucleosome-depleted regions or accessible/open chromatin. Here we ask whether open chromatin profiling can be used to identify the entire repertoire of active promoters and enhancers underlying tissue-specific gene expression during normal development and oncogenesis in vivo. To this end, we first compare two different approaches to detect open chromatin in vivo using the Drosophila eye primordium as a model system: FAIRE-seq, based on physical separation of open versus closed chromatin; and ATAC-seq, based on preferential integration of a transposon into open chromatin. We find that both methods reproducibly capture the tissue-specific chromatin activity of regulatory regions, including promoters, enhancers, and insulators. Using both techniques, we screened for regulatory regions that become ectopically active during Ras-dependent oncogenesis, and identified 3778 regions that become (over-)activated during tumor development. Next, we applied motif discovery to search for candidate transcription factors that could bind these regions and identified AP-1 and Stat92E as key regulators. We validated the importance of Stat92E in the development of the tumors by introducing a loss of function Stat92E mutant, which was sufficient to rescue the tumor phenotype. Additionally we tested if the predicted Stat92E responsive regulatory regions are genuine, using ectopic induction of JAK/STAT signaling in developing eye discs, and observed that similar chromatin changes indeed occurred. Finally, we determine that these are functionally significant regulatory changes, as nearby target genes are up- or down-regulated. In conclusion, we show that FAIRE-seq and ATAC-seq based open chromatin profiling, combined with motif discovery, is a straightforward approach to identify functional genomic regulatory regions, master regulators, and gene regulatory networks controlling complex in vivo processes., Author Summary The functional expression of all genes is regulated by proteins, namely transcription factors that bind to specific areas of DNA known as regulatory regions. Whereas most DNA in our genome is normally bound by other proteins (histones) and packaged into units called nucleosomes, a specific subset of tissue-specific regulatory regions is responsible for tissue-specific gene expression; these active regions are nucleosome-depleted and bound by transcription factors. We use two techniques to identify these open chromatin regions, in a normal tissue and a RasV12 induced cancer tissue. We discovered a remarkable change in the accessible regulatory landscape between these two tissues, with several thousand regions becoming more accessible in the cancer tissue. We identified two transcription factors known to be involved in cancer (AP-1 and Stat92E) controlling these newly accessible regulatory regions. Finally, we introduced a mutation resulting in Stat92E becoming non-functional in the cancer tissue, which decreased the severity of the tumor. Our study shows that open chromatin profiling can be used to identify complex in vivo processes, and we shed new light on Ras dependent cancer development.

Obesity is a common disorder and related diseases such as diabetes, atherosclerosis, hypertension, cardiovascular disease and cancer are a major cause of mortality and morbidity in Western-type societies. Development of obesity is associated with extensive modifications in adipose tissue involving adipogenesis, angiogenesis and extracellular matrix proteolysis. The fibrinolytic (plasminogen/plasmin) and matrix metalloproteinase (MMP) systems cooperate in these processes. A nutritionally induced obesity model in transgenic mice has been used extensively to study the role of the fibrinolytic and MMP systems in the development of obesity. These studies support a role of both systems in adipogenesis and obesity; the role of specific members of these families, however, remains to be determined.

Steroid receptors are transcription factors that, upon binding to their response elements, regulate the expression of several target genes via direct protein interactions with transcriptional coactivators. For the androgen receptor, additional interactions between the amino- and carboxyl-terminal regions have been reported. The first amino acids of the amino-terminal domain are necessary for this amino/carboxyl-terminal interaction. Deletion of a FQNLF core sequence in this region blunts the interaction, as does a G21E mutation. We investigated the effect of the aforementioned mutations in the context of the full size androgen receptor on a series of selective and nonselective androgen response elements. Strikingly, the FQNLF deletion strongly reduced the androgen receptor capacity to transactivate through nonselective motifs but did not affect its activity on selective elements. Although the G21E mutation strongly impairs the amino/carboxyl-terminal interaction, it does not significantly influence androgen receptor activity on either selective or nonselective elements. Surprisingly, this mutation leads to an increased binding of the amino-terminal domain to the glutamine-rich region of the steroid receptor coactivator-1 of the p160 family. Taken together, these data suggest that the amino-terminal amino acids of the androgen receptor play a key role in determining its transcriptional activity by modulating the interaction with the ligand-binding domain as well as interaction with p160 coactivators.

The androgen receptor has a subset of target DNA sequences, which are not recognized by any other steroid receptors. The androgen selectivity of these sequences was proposed to be the consequence of the ability of the androgen receptor to dimerize on direct repeats of 5′-TGTTCT-3′-like sequences. This is in contrast with the classical non-selective elements consisting of inverted repeats of the 5′-TGTTCT-3′ elements separated by three nucleotides and which are recognized by other steroid receptors in addition to the androgen receptor. We demonstrate that while the DNA-binding domain of the oestrogen receptor is unable to dimerize on direct repeats, dimeric binding can be rescued by replacing the second Zn finger and part of the hinge region by the corresponding fragment of the androgen receptor, but not the glucocorticoid receptor. In this study, we investigate the androgen receptor binding to all natural androgen-selective response elements described so far. We show that a 12-amino acid C-terminal extension of the DNA-binding domain is required for high-affinity binding of the androgen receptor to all these elements. For one androgen-specific low-affinity binding site, the flanking sequences do not contribute to the invitro affinity of the androgen receptor DNA-binding domain. Surprisingly, however, they control the transcriptional activity of the androgen receptor in transient transfection experiments. In conclusion, we give evidence that the alternative DNA-dependent dimerization of the androgen receptor on direct repeats is a general mechanism for androgen specificity in which the second Zn finger and hinge region are involved. In addition, the sequences flanking an androgen-response element can control the activity of the androgen receptor.

Transcriptional reprogramming of proliferative melanoma cells into a phenotypically distinct invasive cell subpopulation is a critical event at the origin of metastatic spreading. Here we generate transcriptome, open chromatin and histone modification maps of melanoma cultures; and integrate this data with existing transcriptome and DNA methylation profiles from tumour biopsies to gain insight into the mechanisms underlying this key reprogramming event. This shows thousands of genomic regulatory regions underlying the proliferative and invasive states, identifying SOX10/MITF and AP-1/TEAD as regulators, respectively. Knockdown of TEADs shows a previously unrecognized role in the invasive gene network and establishes a causative link between these transcription factors, cell invasion and sensitivity to MAPK inhibitors. Using regulatory landscapes and in silico analysis, we show that transcriptional reprogramming underlies the distinct cellular states present in melanoma. Furthermore, it reveals an essential role for the TEADs, linking it to clinically relevant mechanisms such as invasion and resistance., SCOPUS: ar.j, info:eu-repo/semantics/published

Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org., Author Summary Gene regulatory networks control developmental, homeostatic, and disease processes by governing precise levels and spatio-temporal patterns of gene expression. Determining their topology can provide mechanistic insight into these processes. Gene regulatory networks consist of interactions between transcription factors and their direct target genes. Each regulatory interaction represents the binding of the transcription factor to a specific DNA binding site near its target gene. Here we present a computational method, called iRegulon, to identify master regulators and direct target genes in a human gene signature, i.e. a set of co-expressed genes. iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10.000 TF motifs and 1000 ChIP-seq data sets or “tracks”. Next, it associates enriched motifs and tracks with candidate transcription factors and determines the optimal subset of direct target genes. We validate iRegulon on ENCODE data, and use it in combination with RNA-seq and ChIP-seq data to map a p53 downstream network with new predicted co-factors and targets. iRegulon is available as a Cytoscape plugin, supporting human, mouse, and Drosophila genes, and provides access to hundreds of cancer-related TF-target subnetworks or “regulons”.

A low-molecular-weight receptor tyrosine kinase inhibitor, 1-(6,7-dihydro-5H-benzo(6,7)cyclohepta(1,2-c)pyridazin-3-yl)-N3-((7-pyrrolidin-1-yl)-6,7,8,9-tetrahydro-5H-benzo(7)annulene-2-yl)-1H-1,2,4-triazole-3,5-diamine (R428) with high affinity and selectivity for the growth arrest-specific protein 6 (GAS6) receptor Axl was used to study a potential role of GAS6 signaling in adiposity. In vitro, R428 caused a concentration-dependent inhibition of preadipocyte differentiation into mature adipocytes, as evidenced by reduced lipid uptake. Inhibition of Axl-mediated signaling was confirmed by reduced levels of phospho-Akt activity. In vivo, oral administration of R428 for 5 weeks to mice kept on a high-fat diet resulted in significantly reduced weight gain and subcutaneous and gonadal fat mass. This was associated with marked adipocyte hypotrophy, enhanced macrophage infiltration, and apoptosis. Thus, affecting GAS6 signaling through receptor antagonism using a low-molecular-weight Axl antagonist impairs adipocyte differentiation and reduces adipose tissue development in a murine model of nutritionally induced obesity.

Summary. Objective: To substantiate a potential role of plasminogen activator inhibitor-1 (PAI-1) in adipogenesis, we have studied its effects on in vitro adipocyte differentiation and on in vivo adipose tissue formation. Results: Our in vitro data do not support a functional role of PAI-1, as substantiated by our findings that: (i) inhibition of PAI-1 with a neutralizing antibody did not affect differentiation of 3T3-F442A preadipocytes; (ii) overexpression of murine PAI-1 in 3T3-F442A cells had no effect on differentiation; and (iii) differentiation of PAI-1-deficient murine embryonic fibroblasts into mature adipocytes was comparable to wild-type (WT) cells. Furthermore, our in vivo studies did not reveal an important role for PAI-1, as suggested by our findings that: (i) de novo fat pad formation in NUDE mice following injection of 3T3-F442A cells was not affected by a PAI-1 neutralizing antibody; and (ii) adipose tissue formation following combined injection of Matrigel and basic fibroblast growth factor was comparable in WT and in PAI-1 deficient mice. Conclusion: Taken together, these in vitro and in vivo studies in murine model systems do not support an important functional role of PAI-1 in adipogenesis.

Placental growth factor (PlGF)-deficient (PlGF-/-) and wild-type mice were kept on a standard-fat or high-fat diet for 15 weeks. With the standard-fat diet, the body weights of PlGF-/- and wild-type mice were comparable, whereas the combined weight of subcutaneous and gonadal adipose tissues was lower in PlGF-/- mice (P = 0.02). With the high-fat diet, PlGF-/- mice had a lower body weight (P < 0.05) and less total subcutaneous plus gonadal adipose tissue (P < 0.0001). Blood vessel size was lower in gonadal adipose tissue of PlGF-/- mice with both the standard-fat and high-fat diet (P < 0.05). Blood vessel density, normalized to adipocyte number, was significantly lower in subcutaneous adipose tissue of PlGF-/- mice fed the high-fat diet (P < 0.01). De novo adipose tissue development in nude mice injected with 3T3-F442A preadipocytes was reduced (P < 0.005) by administration of a PlGF-neutralizing antibody. Bone marrow transplantation from wild-type or PlGF-/- mice to wild-type or PlGF-/- recipient mice revealed significantly lower blood vessel density in PlGF-/- recipient mice without an effect on adipose tissue growth. Thus, in murine models of diet-induced obesity, inactivation of PlGF impairs adipose tissue development, at least in part as a result of reduced angiogenesis. ispartof: Diabetes vol:55 issue:10 pages:2698-2704 status: published

Endocrine disruptors pose a growing threat to human and wildlife health. Validated test systems are required to study the mechanisms by which chemicals may interfere with the endocrine system. In order to identify compounds with (anti)androgenic activity, we used several in vitro bioassays, based on different androgen receptor (AR) functions, including AR transactivation and interaction between the amino-terminal domain and the ligand-binding domain. The AR activity assay, based on activation of the transcription of an androgen-responsive reporter gene in the presence of androgen, proved to excel in terms of high fold induction range and low minimal detection limit. The EC50 value, defined as the concentration that leads to half the maximal response and reflecting the potency of the synthetic androgen R1881 (methyltrienolone), was found to be 250 pM, consistent with the high affinity of this ligand to the human AR. A number of environmental samples were tested in the bioassay. The bioassay also could be used to detect antiandrogenic activity, because known AR-antagonists were able to inhibit R1881-mediated transactivation.

The androgen receptor (AR) is a member of the nuclear receptor superfamily. These ligand-activated transcription factors usually contain two activation functions, a ligand-independent activation function 1(AF1) in the divergent N-terminal domain and a ligand-dependent AF2 in the more conserved C-terminal ligand-binding domain. To promote transcription from target promoters, DNA-bound nuclear receptors recruit coactivator proteins that promote transcription by modifying histones within nucleosomes, resulting in altered topology of chromatin to allow access of the basal transcriptional machinery, or stabilising the pre-initiation complex. It is well known that most coactivators interact with AF2 of many nuclear receptors via conserved, helical LxxLL motifs (where L is leucine and x is any amino acid). The AF2 of the AR is very weak, but we were able to demonstrate that its intrinsic ligand-dependent activity is potentiated by steroid receptor coactivator-1 (SRC1) and that this region interacts with coactivators via LxxLL motifs. However, a mutant SRC1 coactivator with no functional LxxLL motifs was still able to potentiate AR activity. We found that SRC1 can also be recruited to (and increase activity of) AF1 of the AR via a conserved, glutamine-rich region. Point mutations within this region abolish SRC1 interaction with AF1 and also abolish or severely impair its ability to potentiate AR activity on all promoters tested. Thus the AR interacts with SRC1 via two different regions and the AF1 interaction is functionally the more important, although the contribution of the two interactions varies in a promoter-dependent fashion. SRC1 then potentiates receptor activity via recruitment of CBP/p300, a histone acetyltranferase. This is important in the context of prostate cancer as SRC1 and other coactivators including CBP are coexpressed with AR in the luminal epithelial cells of the prostate, where over 90% of prostate tumours arise. There is a need for effective second-line prostate cancer therapy aimed at blocking the AR pathway when anti-androgen therapy has failed. Since there is growing evidence that nuclear receptor cofactors may be implicated in the progression of hormone-dependent tumours to hormone-independent states, novel targets could include the interaction of AR with coactivator proteins. We suggest that the N-terminal interaction would be a more specific and effective target in the case of prostate cancer than the LxxLL/AF2 interaction.

The androgen receptor (AR) is a ligand-dependent transcription factor and belongs to the nuclear receptor family. The AR gene contains a long polymorphic CAG repeat, coding for a polyglutamine tract. In the full size AR, the deletion of the polyglutamine tract results in an increase in the transactivation through canonical AREs. However, this effect is clearly dependent on the response elements, since it is not observed on selective elements. In our assays, a deletion of the repeat positively affected the interactions of the ligand-binding domain with the amino-terminal domain as well as the recruitment of the p160 coactivator SRC-1e to the amino-terminal domain of the AR. This is reflected by an enhanced coactivation of the AR by SRC-1e.

The androgen receptor interacts with the p160 coactivators via two surfaces, one in the ligand binding domain and one in the amino-terminal domain. The ligand binding domain interacts with the nuclear receptor signature motifs, whereas the amino-terminal domain has a high affinity for a specific glutamine-rich region in the p160s. We here describe the implication of two conserved motifs in the latter interaction. The amino-terminal domain of the androgen receptor is a very strong activation domain constituent of Tau5, which is mainly active in the absence of the ligand binding domain, and Tau1, which is only active in the presence of the ligand binding domain. Both domains are, however, implicated in the recruitment of the p160s. Mutation analysis of the p160s has shown that the relative contribution of the two recruitment mechanisms via the signature motifs or via the glutamine-rich region depend on the nature of the enhancers tested. We propose, therefore, that the androgen receptor-coactivator complex has several alternative conformations, depending partially on the context of the enhancer.

Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Single-cell technologies allow measuring chromatin accessibility and gene expression in each cell, but jointly utilizing both layers to map bona fide gene regulatory networks and enhancers remains challenging. Here, we generate independent single-cell RNA-seq and single-cell ATAC-seq atlases of the Drosophila eye-antennal disc and spatially integrate the data into a virtual latent space that mimics the organization of the 2D tissue using ScoMAP (Single-Cell Omics Mapping into spatial Axes using Pseudotime ordering). To validate spatially predicted enhancers, we use a large collection of enhancer-reporter lines and identify ~ 85% of enhancers in which chromatin accessibility and enhancer activity are coupled. Next, we infer enhancer-to-gene relationships in the virtual space, finding that genes are mostly regulated by multiple, often redundant, enhancers. Exploiting cell type-specific enhancers, we deconvolute cell type-specific effects of bulk-derived chromatin accessibility QTLs. Finally, we discover that Prospero drives neuronal differentiation through the binding of a GGG motif. In summary, we provide a comprehensive spatial characterization of gene regulation in a 2D tissue. ispartof: MOLECULAR SYSTEMS BIOLOGY vol:16 issue:5 ispartof: location:England status: published