The dendritic cell (DC) is a cornerstone for a proper adaptive immune response. When a DC detects a pathogen it launches a specific transcriptional program that leads to DC maturation and an ensuing adaptive immune response. To this day, we still do not understand how the underlying transcriptional regulatory networks regulate DC maturation in response to specific stimuli including pathogen detection. Without detailed knowledge of these regulatory networks it is impossible to predict the impact of genomic variations on the capacity of DCs to mount a proper response. More generally, a better understanding of how specific gene regulatory networks operate during DC maturation can be used to control DCs and has the potential for a broad-spectrum of applications reaching from improved vaccination efforts to controlling autoimmunity. We have previously mapped the epigenetic cis-regulatory landscape of mouse dendritic cells by using a combination of transcriptional profiling (RNA-Seq) and annotation of chromatin occupancy of over 30 different transcription factors and modified histones (ChIP-Seq). The regulatory network models we built from these datasets are the basis for this proposal. Until recently, a thorough functional characterization of these regulatory networks was out of reach. The proposal outlined here describes the use a CRISPR-effector technology we recently developed, to functionally dissect the transcriptional network operating during DC maturation. This innovative technology not only allows for a rapid functional annotation of individual regulatory elements but also allows for combinatorial inactivation of gene coding and non-coding genomic regions. Here we propose to probe both cis- and trans- effectors that regulate the maturation of primary DCs derived from CRISPR-effector mice in response to lipopolysaccharide (LPS). We propose a network perturbation approach that targets network nodes with distinct regulatory patterns to test and refine previously identified regulatory network models. In the first Aim we repress key trans activators by delivering a KRAB domain to key transcription factors. We have previously shown that this repressor is extremely effective in silencing transcription when delivered to gene promoters. We will also explore network molarity by dissecting Tumor Necrosis Factor (TNF) signaling, a network component critical for the late phase of DC maturation after LPS stimulation. In the second aim we deliver an enhancer specific CRISPR-effector (LSD1) to key cis- regulatory regions to perturb specific enhancers activity. Here, we will test whether differential enhancer activity affects their targets and the regulatory network. The latter will hae important implications for genomic variation in cis-regulatory elements, a feature under active investigation in the context of genome wide association studies.
The maturation of dendritic cells to specific stimuli, and in turn the outcome of an immune response, is critically dependent on not well-characterized gene regulatory networks. Herein, we propose utilizing novel mouse models to define functional components of gene regulatory networks that orchestrate the response of dendritic cells to detection of pathogens. The project proposed here would be allow us to build predictive models of the impact of disruptions in the network components that can eventually have impact reaching from vaccine development to control of autoimmunity.
| Donnard, Elisa; Vangala, Pranitha; Afik, Shaked et al. (2018) Comparative Analysis of Immune Cells Reveals a Conserved Regulatory Lexicon. Cell Syst 6:381-394.e7 |
| Parsi, Krishna Mohan; Hennessy, Erica; Kearns, Nicola et al. (2017) Using an Inducible CRISPR-dCas9-KRAB Effector System to Dissect Transcriptional Regulation in Human Embryonic Stem Cells. Methods Mol Biol 1507:221-233 |
| Derr, Alan; Yang, Chaoxing; Zilionis, Rapolas et al. (2016) End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data. Genome Res 26:1397-1410 |
| Genga, Ryan M; Kearns, Nicola A; Maehr, René (2016) Controlling transcription in human pluripotent stem cells using CRISPR-effectors. Methods 101:36-42 |
