Integrative analysis of regulatory tracks for the identification of direct TF-target interactions

status: accepte

Identification of lineage-specific Cis-regulatory modules associated with variation in transcription factor binding and chromatin activity using Ornstein-Uhlenbeck models

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

Identification of Lineage-Specific Cis-Regulatory Modules Associated with Variation in Transcription Factor Binding and Chromatin Activity Using Ornstein-Uhlenbeck Models

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.status: publishe

EXPLORING THE p53 TRANSCRIPTIONAL NETWORK THROUGH INTEGRATIVE GENOMICS

status: accepte

Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference

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

Natural Variation of Chromatin Accessibility reveals a new epithelial Enhancer model

Enhancers coordinate gene expression levels and the majority of disease related SNPs are located in these non-coding regions. Yet how genomic variation affects enhancer function is poorly understood. To investigate cis-regulatory variation in vivo, in an endogenous enhancer context, we profiled the genome-wide chromatin accessibility of epithelial tissues (imaginal discs), across 30 inbred Drosophila lines from the DGRP project. Statistical analysis identified 4289 chromatin accessibility QTLs (caQTL). We singled out the transcription factor Grainyhead as a key player, for which 70 caQTLs alter its binding site, thereby causing a concordant gain or loss in chromatin accessibility and in vivo enhancer activity. We show a clear uncoupling between enhancer accessibility and activity using an in vivo enhancer-reporter screen combined with cell-sorted ATAC-seq. The epithelial enhancers become accessible in all cells, due to the binding of the master regulatory Grainyhead, but activate gene expression only in a specific subpopulation of cells. Finally, to discriminate between functional and non-functional Grainyhead recognition motifs in the genome, we trained various machine learning algorithms and compared enhancers across different Drosophila species, allowing the identification of key enhancer features required for Grainyhead binding. In conclusion, we find a new epithelial enhancer model in which Grainyhead plays a similar role in epithelial tissues as Zelda in the embryo, uncoupling chromatin accessibility and enhancer activity for thousands of epithelial enhancers.status: publishe

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

Abstract The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying “personal” gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .status: publishe

Expression and chromatin profiling of melanoma reveals novel candidate master regulators supporting the phenotype switching model

Melanoma is one of the most aggressive cancers to date and is marked by a therapy-resistant character reflecting its high heterogeneity and plasticity. Expression profiling of melanoma shows the presence of at least two distinct transcriptional programs within the tumor reflecting different cellular states: more proliferative versus invasive and migratory cells. These states are defined within a phenotype switching model, a process that is thought to be driven by the cells microenvironment rather then by mutations. Besides characterizing each state with a specifc expression program, this model attributes cells with the capacity to switch back and forth between each cellular state. However, the underlying molecular mechanisms and the origin of these states are poorly understood. Identifying the key regulatory networks behind them would mean a great advancement in comprehending melanoma. To investigate this, we combined publicly available data from TCGA, ENCODE and GEO with in-house datasets generated from patient-derived cultures. Besides gene expression data, we applied FAIRE-seq and ChIP-seq against both H3K27ac and H3K27me3 to investigate the chromatin activity. In addition, we used a large collection of transcription factor binding motifs allowing us to identify master regulators within each state. Together, this data enabled us to identify SOX10/MITF and AP1/STAT/TEAD as potential master regulators for the proliferative and invasive state. In addition, the chromatin data allowed us to pinpoint distal enhancers controlled by these factors. Finally, to support our findings, we performed a perturbation of TEAD to verify its role in the invasive state of melanoma tumor cells. All together, by collecting and mining through an extensive set of data we gained insight into the mechanisms supporting melanoma’s plasticity and heterogeneity. This itself is a step forward and can proof to be vital for the development of more effective therapies to battle melanoma.status: accepte