Annelien Verfaillie

iRegulon: from a gene list to a gene regulatory network using large motif and track collections.

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.

p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity

In a search for mediators of the p53 tumor suppressor pathway, which induces pleiotropic and often antagonistic cellular responses, we identified the long noncoding RNA (lncRNA) NEAT1. NEAT1 is an essential architectural component of paraspeckle nuclear bodies, whose pathophysiological relevance remains unclear. Activation of p53, pharmacologically or by oncogene-induced replication stress, stimulated the formation of paraspeckles in mouse and human cells. Silencing Neat1 expression in mice, which prevents paraspeckle formation, sensitized preneoplastic cells to DNA-damage-induced cell death and impaired skin tumorigenesis. We provide mechanistic evidence that NEAT1 promotes ATR signaling in response to replication stress and is thereby engaged in a negative feedback loop that attenuates oncogene-dependent activation of p53. NEAT1 targeting in established human cancer cell lines induced synthetic lethality with genotoxic chemotherapeutics, including PARP inhibitors, and nongenotoxic activation of p53. This study establishes a key genetic link between NEAT1 paraspeckles, p53 biology and tumorigenesis and identifies NEAT1 as a promising target to enhance sensitivity of cancer cells to both chemotherapy and p53 reactivation therapy.

Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state

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.

Evidence for DNA-binding domain--ligand-binding domain communications in the androgen receptor.

ABSTRACT DNA binding as well as ligand binding by nuclear receptors has been studied extensively. Both binding functions are attributed to isolated domains of which the structure is known. The crystal structure of a complete receptor in complex with its ligand and DNA-response element, however, has been solved only for the peroxisome proliferator-activated receptor γ (PPARγ)-retinoid X receptor α (RXRα) heterodimer. This structure provided the first indication of direct interactions between the DNA-binding domain (DBD) and ligand-binding domain (LBD). In this study, we investigated whether there is a similar interface between the DNA- and ligand-binding domains for the androgen receptor (AR). Despite the structural differences between the AR- and PPARγ-LBD, a combination of in silico modeling and docking pointed out a putative interface between AR-DBD and AR-LBD. The surfaces were subjected to a point mutation analysis, which was inspired by known AR mutations described in androgen insensitivity syndromes and prostate cancer. Surprisingly, AR-LBD mutations D695N, R710A, F754S, and P766A induced a decrease in DNA binding but left ligand binding unaffected, while the DBD-residing mutations K590A, K592A, and E621A lowered the ligand-binding but not the DNA-binding affinity. We therefore propose that these residues are involved in allosteric communications between the AR-DBD and AR-LBD.

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

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.

iRegulon and i‐cisTarget: Reconstructing Regulatory Networks Using Motif and Track Enrichment

Gene expression profiling is often used to identify genes that are co‐expressed in a biological process or disease. Downstream analyses of co‐expressed gene sets using bioinformatics methods can reveal candidate transcription factors (TF) that co‐regulate these genes, based on the presence of shared TF binding sites. Drawing gene regulatory networks that connect TFs to their predicted target genes can uncover gene modules that implement a particular function. Here, we describe several protocols to analyze any set of co‐expressed genes using iRegulon and i‐cisTarget. These tools perform regulatory sequence analysis (motif discovery) and integrate and mine large collections of existing regulatory data, such as ChIP‐Seq, DHS‐seq, and FAIRE‐seq (track discovery). While iRegulon focuses on sets of co‐expressed genes, i‐cisTarget also analyses genomic regions as input. The following protocols describe how to install and use these tools, how to interpret the obtained results, and will thus help to create meaningful regulatory networks.