Abstract
Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in development, tumour progression and fibrosis. Predicted “neutral” (non-functional) TF binding always accounted for the majority (50% to 95%) of candidate target genes from statistically significant peaks and HOT regions had higher functional binding than most of the Snail and Twist datasets examined. Our results illuminated conserved gene networks that control epithelial plasticity in development and disease. We identified new gene functions and network modules including crosstalk with notch signalling and regulation of chromatin organisation, evidencing networks that reshape Waddington’s epigenetic landscape during epithelial remodelling. Expression of orthologous functional TF targets discriminated breast cancer molecular subtypes and predicted novel tumour biology, with implications for precision medicine. Predicted invasion roles were validated using a tractable cell model, supporting our approach.
Highlights
Transcriptional regulatory factors (TFs) govern gene expression, which is a crucial determinant of phenotype
We developed an algorithm (NetNC) with capability for context-specific functional TF target discovery and applied this to study the epithelial to mesenchymal transition (EMT) TFs Snail and Twist
We developed and validated the novel Network Neighbourhood Clustering (NetNC) algorithm for identification of biologically coherent transcription factor (TF) target genes, and a comprehensive D. melanogaster functional gene network (DroFN)
Summary
Transcriptional regulatory factors (TFs) govern gene expression, which is a crucial determinant of phenotype. TF binding sites may be determined using chromatin immunoprecipitation followed by sequencing (ChIP-seq) or microarray (ChIP-chip). These and related methods (e.g., ChIP-exo, DamID) have revealed a substantial proportion of statistically significant “neutral” TF binding, that has apparently no effect on transcription from assigned target genes [1,7,8,9]. A considerable proportion of individual TF binding events at HOT regions may have little effect on gene expression and association with chromatin accessibility suggests non-canonical regulatory function such as sequestration of TFs or in 3D genome organization [13,14], as well as possible technical artefacts [15]. While recent integrative work enhances context-independent TF target prediction [17], identification of bona fide functional TF target genes remains a major obstacle in understanding the regulatory networks that control cell behaviour [2,5,9,18,19]
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