14 research outputs found
Cell type directed design of synthetic enhancers
No full text<p>All the code, model files, and data associated with the manuscript : 'Cell type directed design of synthetic enhancers' can be found here.</p>
Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
No full textGenomic 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.jinfo:eu-repo/semantics/publishe
Genome Research / Cross-species analysis of enhancer logic using deep learning
No full textDeciphering 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
Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics
No full textAbstract 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
Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics
No full textSingle-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.sponsorship: This work is funded by an ERC Consolidator Grant to S. Aerts (724226_cis-CONTROL), by the Special Research Fund (BOF) KU Leuven (grant PF/10/016, to S. Aerts) and F.W.O (grants G.0791.14, G.0C04.17 to S.Aerts and PhD fellowship 11F1519N to C.B.G.-B). Stocks obtained from the Bloomington Drosophila Stock Center were used in this study. Single-cell infrastructure was funded by the Hercules Foundation (grant no. AKUL/13/41). Computing was performed at the Vlaams Supercomputer Center (VSC). The VIB BioImaging Core (Leuven platform) provided valuable insight on image processing. The authors thank to Maximilian Haeussler and Kate Rosenbloom (UCSC Genome Browser) for their help in data visualization on the UCSC Genome Browser; to the various groups that make curated position weight matrices publicly available, including T. Hughes (cis-bp), M. Bulyk (UniPROBE), A. Mathelier (JASPAR), V. Makeev (Hocomoco), and many others; to the Janelia FlyLight Project for publicly providing images and reporter lines to assess enhancer activity on imaginal discs and CNS in Drosophila; and to the ENCODE Consortium for publicly providing raw and processed data of a wide range of genomic assays. (ERC Consolidator Grant|724226_cis-CONTROL, Special Research Fund (BOF) KU Leuven|PF/10/016, F.W.O|G.0791.14, F.W.O|G.0C04.17, F.W.O|11F1519N, Hercules Foundation|AKUL/13/41)status: Publishe
Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics
No full textSingle-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.sponsorship: This work is funded by an ERC Consolidator Grant to S. Aerts (724226_cis-CONTROL), by the Special Research Fund (BOF) KU Leuven (grant PF/10/016, to S. Aerts) and F.W.O (grants G.0791.14, G.0C04.17 to S.Aerts and PhD fellowship 11F1519N to C.B.G.-B). Stocks obtained from the Bloomington Drosophila Stock Center were used in this study. Single-cell infrastructure was funded by the Hercules Foundation (grant no. AKUL/13/41). Computing was performed at the Vlaams Supercomputer Center (VSC). The VIB BioImaging Core (Leuven platform) provided valuable insight on image processing. The authors thank to Maximilian Haeussler and Kate Rosenbloom (UCSC Genome Browser) for their help in data visualization on the UCSC Genome Browser; to the various groups that make curated position weight matrices publicly available, including T. Hughes (cis-bp), M. Bulyk (UniPROBE), A. Mathelier (JASPAR), V. Makeev (Hocomoco), and many others; to the Janelia FlyLight Project for publicly providing images and reporter lines to assess enhancer activity on imaginal discs and CNS in Drosophila; and to the ENCODE Consortium for publicly providing raw and processed data of a wide range of genomic assays. (ERC Consolidator Grant|724226_cis-CONTROL, Special Research Fund (BOF) KU Leuven|PF/10/016, F.W.O|G.0791.14, F.W.O|G.0C04.17, F.W.O|11F1519N, Hercules Foundation|AKUL/13/41)status: Publishe
CNKSR1 gene defect can cause syndromic autosomal recessive intellectual disability
No full textThe advent of high-throughput sequencing technologies has led to an exponential increase in the identification of novel disease-causing genes in highly heterogeneous diseases. A novel frameshift mutation in CNKSR1 gene was detected by Next-Generation Sequencing (NGS) in an Iranian family with syndromic autosomal recessive intellectual disability (ARID). CNKSR1 encodes a connector enhancer of kinase suppressor of Ras 1, which acts as a scaffold component for receptor tyrosine kinase in mitogen-activated protein kinase (MAPK) cascades. CNKSR1 interacts with proteins which have already been shown to be associated with intellectual disability (ID) in the MAPK signaling pathway and promotes cell migration through RhoA-mediated c-Jun N-terminal kinase (JNK) activation. Lack of CNKSR1 transcripts and protein was observed in lymphoblastoid cells derived from affected patients using qRT-PCR and western blot analysis, respectively. Furthermore, RNAi-mediated knockdown of cnk, the CNKSR1 orthologue in Drosophila melanogaster brain, led to defects in eye and mushroom body (MB) structures. In conclusion, our findings support the possible role of CNKSR1 in brain development which can lead to cognitive impairment
