Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes

Abstract

We take a comprehensive approach to the study of regulatory control of gene expression in melanocytes that proceeds from large-scale enhancer discovery facilitated by ChIP-seq; to rigorous validation in silico, in vitro, and in vivo; and finally to the use of machine learning to elucidate a regulatory vocabulary with genome-wide predictive power. We identify 2489 putative melanocyte enhancer loci in the mouse genome by ChIP-seq for EP300 and H3K4me1. We demonstrate that these putative enhancers are evolutionarily constrained, enriched for sequence motifs predicted to bind key melanocyte transcription factors, located near genes relevant to melanocyte biology, and capable of driving reporter gene expression in melanocytes in culture (86%; 43/50) and in transgenic zebrafish (70%; 7/10). Next, using the sequences of these putative enhancers as a training set for a supervised machine learning algorithm, we develop a vocabulary of 6-mers predictive of melanocyte enhancer function. Lastly, we demonstrate that this vocabulary has genome-wide predictive power in both the mouse and human genomes. This study provides deep insight into the regulation of gene expression in melanocytes and demonstrates a powerful approach to the investigation of regulatory sequences that can be applied to other cell types.

High-throughput functional testing of ENCODE segmentation predictions

“…We found that these predictions were cell type-specific in K562 cells and could accurately distinguish enhancer sequences from non-enhancer sequences. Our results suggest that combinations of TF-binding preferences, not histone modifications alone, are most predictive of actively expressing genomic sequences, a result supported by other attempts to define the sequence features of enhancers (Heinz et al 2010;Lee et al 2011;Arvey et al 2012;Gorkin et al 2012;Smith et al 2013). These results support a model where TF binding and subsequent tran- (Struhl and Segal 2013), leading to the constellation of histone modifications observed in segments with high cis-regulatory activity.…”

A Polymorphism in IRF4 Affects Human Pigmentation through a Tyrosinase-Dependent MITF/TFAP2A Pathway

“…The corresponding patterns of DNase I HS in these cell types suggest that IRF4 expression in lymphocytes and melanocytes are directed by distinct sets of regulatory elements (Figure 2b). In addition, the position orthologous to rs12203592 in the mouse genome directly overlaps a melanocyte enhancer (Gorkin et al, 2012) since it is occupied by p300 and marked by H3K4me1 in the melanocyte line melan-Ink4a-Arf (Sviderskaya et al, 2002) (Figure 2c). …”

“…c) UCSC genome browser view of 25 KB region around Irf4 (mm9 coordinates chr13:30,838,000–30,863,000). The ChIP-seq signals for EP300 (green) and H3K4me1 (blue) in the mouse melanocyte line melan-Ink4a-Arf from Gorkin et al, 2012.…”

Enhancer-targeted genome editing selectively blocks innate resistance to oncokinase inhibition

“…2B). While MYC ChIPseq peaks were the most enriched feature for this analysis, we prioritized MITF due to its role as a master regulator (Steingrímsson et al 2004;Levy et al 2006), lineage-specific oncogene (Garraway et al 2005;Tsao et al 2012), and its association with MET regulation (McGill et al 2006;Beuret et al 2007) and enhancer function (Gorkin et al 2012). These results raised the possibility that MITF may utilize lineage-specific regulatory elements to control MET expression in melanoma downstream from BRAF inhibition.…”