Systematic dissection of genomic features determining transcription factor binding and enhancer function

Abstract

Enhancers regulate gene expression through the binding of sequence-specific transcription factors (TFs) to cognate motifs. Various features influence TF binding and enhancer function-including the chromatin state of the genomic locus, the affinities of the binding site, the activity of the bound TFs, and interactions among TFs. However, the precise nature and relative contributions of these features remain unclear. Here, we used massively parallel reporter assays (MPRAs) involving 32,115 natural and synthetic enhancers, together with high-throughput in vivo binding assays, to systematically dissect the contribution of each of these features to the binding and activity of genomic regulatory elements that contain motifs for PPARγ, a TF that serves as a key regulator of adipogenesis. We show that distinct sets of features govern PPARγ binding vs. enhancer activity. PPARγ binding is largely governed by the affinity of the specific motif site and higher-order features of the larger genomic locus, such as chromatin accessibility. In contrast, the enhancer activity of PPARγ binding sites depends on varying contributions from dozens of TFs in the immediate vicinity, including interactions between combinations of these TFs. Different pairs of motifs follow different interaction rules, including subadditive, additive, and superadditive interactions among specific classes of TFs, with both spatially constrained and flexible grammars. Our results provide a paradigm for the systematic characterization of the genomic features underlying regulatory elements, applicable to the design of synthetic regulatory elements or the interpretation of human genetic variation.gene regulation | transcription factor binding | systems biology

NFIA co-localizes with PPARγ and transcriptionally controls the brown fat gene program

“…6a–d). Supportively, genetic variations which alter NF-1 motifs were reported to affect not only the binding of NFI, but also the binding of PPARγ 31 , chromatin accessibility 32 , and the enhancer activity 33 . Proximally co-occupied transcription factors often compete with a nucleosome to access DNA 34 , and co-localization of NFIA and PPARγ more likely to result in nucleosome displacement than binding of NFIA or PPARγ alone, probably leading to increased chromatin accessibility, enhancer activity and gene expression.…”

Linear models enable powerful differential activity analysis in massively parallel reporter assays

“…Characterization studies examine thousands of different putative regulatory elements that have a wide variety of sequence features and try to correlate these sequence features with measured activity levels (Grossman et al, 2017;Guo et al, 2017;Safra et al, 2017;Levo et al, 2017;Maricque, Dougherty, and Cohen, 2017;Groff et al, 2016;Ernst et al, 2016;White, Kwasnieski, et al, 2016;Ferreira et al, 2016;Fiore and Cohen, 2016;Farley et al, 2015;Kamps-Hughes et al, 2015;Dickel et al, 2014;Kwasnieski, Fiore, et al, 2014;Mogno, Kwasnieski, and Cohen, 2013;Gisselbrecht et al, 2013;White, Myers, et al, 2013;Smith et al, 2013). Typical statistical analyses use regression to study the impact of multiple features simultaneously.…”

Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

“…Previous work have used single MPRA data sets to better identify functional DNA sequences and then study the features that make a sequence regulatory active (Grossman et al, 2017;Lee et al, 2015;Sharon et al, 2012). For example, in the expression quantitative trait loci (eQTL) causal SNP challenge of the Fourth Critical Assessment of Genome Interpretation (CAGI4) community experiment, participants developed methods for predicting regulatory activity of candidate genomic regions and the effect of minor variants on their regulatory potential in MPRA (Beer, 2017;Kreimer et al, 2017;Zeng, Edwards, Guo, & Gifford, 2017).…”