SUMMARY Recent studies suggest a hierarchical model in which lineage-determining factors act in a collaborative manner to select and prime cell-specific enhancers, thereby enabling signal-dependent transcription factors to bind and function in a cell type-specific manner. Consistent with this model, TLR4 signaling primarily regulates macrophage gene expression through a pre-existing enhancer landscape. However, TLR4 signaling also induces priming of ~3000 enhancer-like regions de novo, enabling visualization of intermediates in enhancer selection and activation. Unexpectedly, we find that enhancer transcription precedes local mono- and di-methylation of histone H3 lysine 4 (H3K4me1/2). H3K4 methylation at de novo enhancers is primarily dependent on the histone methyltransferases Mll1, Mll2/4 and Mll3, and is significantly reduced by inhibition of RNA polymerase II elongation. Collectively, these findings suggest an essential role of enhancer transcription in H3K4me1/2 deposition at de novo enhancers that is independent of potential functions of the resulting eRNA transcripts.

The mechanisms by which genetic variation affects transcription regulation and phenotypes at the nucleotide level are incompletely understood. Here, we use natural genetic variation as an in vivo mutagenesis screen to assess the genome-wide effects of sequence variation on lineage-determining and signal-specific transcription factor binding, epigenomics, and transcriptional outcomes in primary macrophages from different mouse strains. We find substantial genetic evidence supporting the concept that lineage-determining transcription factors (LDTFs) define epigenetic and transcriptomic states by selecting enhancer-like regions in the genome in a collaborative fashion and facilitating binding of signal-dependent factors. This hierarchical model of transcription factor function suggests that limited sets of genomic data for LDTFs and informative histone modifications can be used for prioritization of disease-associated regulatory variants.

Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of mouse CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis plays an important role in translating TCR signal strength into proportional activation of enhancers and genes essential for T cell function.DOI: http://dx.doi.org/10.7554/eLife.10134.001

Highlights d FOXO1 opposes senescence and the effector program in T cells d FOXO1 directly binds genomic loci of AP-1 subunits and AP-1 repressor BACH2 d Age and progressive cellular differentiation each decrease FOXO1 and increase AP-1

Global run-on sequencing (GRO-seq) is a recent addition to the series of high-throughput sequencing methods that enables new insights into transcriptional dynamics within a cell. However, GRO-sequencing presents new algorithmic challenges, as existing analysis platforms for ChIP-seq and RNA-seq do not address the unique problem of identifying transcriptional units de novo from short reads located all across the genome. Here, we present a novel algorithm for de novo transcript identification from GRO-sequencing data, along with a system that determines transcript regions, stores them in a relational database and associates them with known reference annotations. We use this method to analyze GRO-sequencing data from primary mouse macrophages and derive novel quantitative insights into the extent and characteristics of non-coding transcription in mammalian cells. In doing so, we demonstrate that Vespucci expands existing annotations for mRNAs and lincRNAs by defining the primary transcript beyond the polyadenylation site. In addition, Vespucci generates assemblies for un-annotated non-coding RNAs such as those transcribed from enhancer-like elements. Vespucci thereby provides a robust system for defining, storing and analyzing diverse classes of primary RNA transcripts that are of increasing biological interest.