Genomic enhancers are important regulators of gene expression, but their identification is a challenge, and methods depend on indirect measures of activity. We developed a method termed STARR-seq to directly and quantitatively assess enhancer activity for millions of candidates from arbitrary sources of DNA, which enables screens across entire genomes. When applied to the Drosophila genome, STARR-seq identifies thousands of cell type-specific enhancers across a broad continuum of strengths, links differential gene expression to differences in enhancer activity, and creates a genome-wide quantitative enhancer map. This map reveals the highly complex regulation of transcription, with several independent enhancers for both developmental regulators and ubiquitously expressed genes. STARR-seq can be used to identify and quantify enhancer activity in other eukaryotes, including humans.

Steroid hormones act as important developmental switches, and their nuclear receptors regulate many genes. However, few hormone-dependent enhancers have been characterized, and important aspects of their sequence architecture, cell-type-specific activating and repressing functions, or the regulatory roles of their chromatin structure have remained unclear. We used STARR-seq, a recently developed enhancer-screening assay, and ecdysone signaling in two different Drosophila cell types to derive genome-wide hormone-dependent enhancer-activity maps. We demonstrate that enhancer activation depends on cis-regulatory motif combinations that differ between cell types and can predict cell-type-specific ecdysone targeting. Activated enhancers are often not accessible prior to induction. Enhancer repression following hormone treatment seems independent of receptor motifs and receptor binding to the enhancer, as we show using ChIP-seq, but appears to rely on motifs for other factors, including Eip74. Our strategy is applicable to study signal-dependent enhancers for different pathways and across organisms.

No abstract

The mapping of molecular inputs to their molecular outputs (input/output, I/O mapping) is an important characteristic of gene circuits, both natural and synthetic. Experimental determination of such mappings for synthetic circuits is best performed using stably integrated genetic constructs. In mammalian cells, stable integration of complex circuits is a time-consuming process that hampers rapid characterization of multiple circuit variants. On the other hand, transient transfection is quick. However, it is an extremely noisy process and it is unclear whether the obtained data have any relevance to the input/output mapping of a circuit obtained in the case of a stable integration. Here we describe a data processing workflow, Peakfinder algorithm for flow cytometry data (PFAFF), that allows extracting precise input/output mapping from single-cell protein expression data gathered by flow cytometry after a transient transfection. The workflow builds on the numerically-proven observation that the multivariate modes of input and output expression of multi-channel flow cytometry datasets, pre-binned by the expression level of an independent transfection reporter gene, harbor cells with circuit gene copy numbers distributions that depend deterministically on the properties of a bin. We validate our method by simulating flow cytometry data for seven multi-node circuit architectures, including a complex bi-modal circuit, under stable integration and transient transfection scenarios. The workflow applied to the simulated transient transfection data results in similar conclusions to those reached with simulated stable integration data. This indicates that the input/output mapping derived from transient transfection data using our method is an excellent approximation of the ground truth. Thus, the method allows to determine input/output mapping of complex gene network using noisy transient transfection data.

No abstract