iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

Abstract

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.

Multiplex enhancer-reporter assays uncover unsophisticated TP53 enhancer logic

“…These enriched DNA fragments, averaging 500 base pairs, are then cloned upstream of a fluorescent protein reporter preceded by a minimal promoter and a synthetic intron (Arnold et al 2013) and followed by a 17 base pair random barcode allowing for 17 × 10 9 possible barcodes (Methods). Using CHEQ-seq, we tested the enhancer activity of 1526 TP53 ChIP-seq peaks obtained in MCF7 breast cancer cells treated with Nutlin-3a (Janky et al 2014). Additionally, 94 promoters of housekeeping genes (HKG) were selected as control regions, assuming that they drive stable reporter gene expression independent of any perturbation (Eisenberg and Levanon 2013).…”

“…4A). To test whether indirect peaks may have another regulatory function, we first predicted which ChIP-seq peaks within the full set of 3634 TP53 ChIP-seq peaks (Janky et al 2014) are likely directly bound based on the presence of a TP53 motif. To this end, we used a random forest model trained on the CHEQseq positive set, with the nine TP53 PWMs identified above (Supplemental Fig.…”

“…Cold Spring Harbor Laboratory Press on April 2, 2019 -Published by genome.cshlp.org Downloaded from against input (GSE47043) as described before (Janky et al 2014) and were filtered for centromere and telomere regions and ranked based on their peak score. The top 1700 regions were selected for bait design as described in Supplemental Materials and Methods.…”

“…MCF7 cells (TP53 wild type or TP53 knockdown) were cultured and transfected as described before (Janky et al 2014). DNA and RNA were extracted and cDNA prepared according to the manufacturer's guidelines.…”

“…RNA-seq for MCF7 TP53 knockdown was extracted and performed as described previously (Janky et al 2014). The collected public ChIP-seq data against TP53 are summarized in Supplemental Table S7.…”