Synthetic and genomic regulatory elements reveal aspects of cis-regulatory grammar in mouse embryonic stem cells

Abstract

In embryonic stem cells (ESCs), a core transcription factor (TF) network establishes the gene expression program necessary for pluripotency. To address how interactions between four key TFs contribute to cis-regulation in mouse ESCs, we assayed two massively parallel reporter assay (MPRA) libraries composed of binding sites for SOX2, POU5F1 (OCT4), KLF4, and ESRRB. Comparisons between synthetic cis-regulatory elements and genomic sequences with comparable binding site configurations revealed some aspects of a regulatory grammar. The expression of synthetic elements is influenced by both the number and arrangement of binding sites. This grammar plays only a small role for genomic sequences, as the relative activities of genomic sequences are best explained by the predicted occupancy of binding sites, regardless of binding site identity and positioning. Our results suggest that the effects of transcription factor binding sites (TFBS) are influenced by the order and orientation of sites, but that in the genome the overall occupancy of TFs is the primary determinant of activity.

Base-resolution models of transcription factor binding reveal soft motif syntax

“…Finally, it is difficult to make conclusions about the impact of an over-represented syntactic property on cooperative TF binding without systematic perturbation experiments. These issues are typically resolved experimentally by performing in vitro binding experiments using libraries of carefully designed synthetic sequences that sample desired properties of interest 6,17,112,119,120 .…”

“…[5][6][7][8][9][10][11][12] . However, genome-wide analyses have rarely identified statistically over-represented motif syntax rules, questioning whether they exist and impose evolutionarily constraints on enhancer function [13][14][15][16][17] . When patterns are discovered computationally [18][19][20][21][22][23][24] , they are difficult to validate experimentally and the mechanism by which they might affect TF cooperativity is not clear.…”

Deciphering the regulatory logic of aDrosophilaenhancer through systematic sequence mutagenesis and quantitative image analysis

“…Yet, the overall transcriptional levels of developmental genes must be tightly controlled for normal development, as gain-of-function and hypomorph mutants, or RNA-seq experiments show. A number of studies have taken activity levels into account in individual cells in cell culture experiments (e.g., (King et al, 2020;Kircher et al, 2019;Kwasnieski et al, 2012)) or dissociated tissue (e. g., (Farley et al, 2015)), but in this case the information on spatio-temporal variation (the pattern), is lost. By contrast, other studies have quantified pattern elements of enhancer activity but with limited spatial resolution (Crocker et al, 2015;Dufourt et al, 2018)).…”

The Oft-Overlooked Massively Parallel Reporter Assay: Where, When, and Which Psychiatric Genetic Variants are Functional?

“…MPRAs thus offer a flexible framework to study regulatory phenomenon, including transcription factor (TF) and RNA binding protein (RBP) actions, transcript stability, and ribosome occupancy. MPRAs have been most broadly applied to explore and computationally model "regulatory grammar" of transcriptional regulators: how sequence features such as binding motifs, their abundance, and arrangement affect regulatory capacity (31)(32)(33)(34)(35)(36)(37)(38). More recently, these approaches have been applied to identify the transcriptional consequences of SNPs and rare variants (39)(40)(41)(42)(43)(44)(45).…”

“…Expression-representing transcription or RNA stability-is typically measured as the counts of RNA barcode per encoding DNA barcode (Figure 1G). To define active or differentially active elements, expression levels can be normalized to e.g., a minimalpromoter only set of barcodes (31,34,37,(47)(48)(49), compared between alleles (39)(40)(41)(42)(43)(44)(45), or compared to shuffled parent sequence(s) (32,37) MPRAs also enable study of post-transcriptional regulatory elements. As shown in Figure 1C and 1D, the same architecture and RNA/DNA expression metric can be used to assess UTR effects on transcript stability.…”