The developmental shifts that occur when cells respond to environmental stimuli are controlled in large part by gene expression programs involving thousands of genes. Transcription factors (TFs), chromatin modifying enzymes, and cis-acting DNA elements contribute to the networks that underlie such programs. The code that links these variables in such a way that the expression of a given gene can be predicted based on the presence of specific components has yet to be deciphered. A model for such a code will be constructed here based on genome-wide analysis of human dendritic cells (DCs) as they mature in response to pathogens. DCs are antigen-presenting cells that initiate and determine the quality and magnitude of the host immune response. Recent technical advances in stem cell biology, reverse-genetic tools for primary human cells, and genome-wide assessment of transcripts, local chromatin features and long-range chromatin interactions, will be exploited here to construct a model for the transcriptional regulatory network that underlies pathogen detection and maturation in human DCs.
In Aim 1, DCs will be sampled in a time-course following stimulation with LPS. Stimulation-responsive genes will be identified by RNA-Seq. Chromatin features will be mapped using ChIP-Seq, high-resolution global DNA foot printing, and Hi-C. This systematic map of all responsive genes and the regulatory regions associated with them will be combined with known DNA-binding motifs to generate an initial model for the gene regulatory network.
In Aim 2 sixty transcription factors that drive transcription within the first 2 hrs of LPS stimulation will be identified from the model in Aim 1. These factors will be knocked-down in hDCs and the effect on the LPS response will be assessed using RNA-Seq. This functional data for each TF will be complemented by measurement of protein-DNA interactions using ChIP- Seq; the later will exploit CRISPR technology to fuse a common epitope tag to endogenous coding sequences for these TFs. By pinpointing key transcription factors, their binding sites, and transcriptionally-responsive genes, this analysis will be used to refine the model for the gene regulatory network.
In Aim 3, a subset of cis- acting regulatory regions important for controlling the hDC transcriptional response to LPS will be identified using features collected in the previous aims. Selected loci will be perturbed using CRISPR technology and effects on target gene expression will be examined. The effect on gene expression of allelic variants in relevant loci will be assessed, using published data as well as 300 human samples that will be genotyped using Pac- Bio. In conjunction, the variance in the higher-order chromatin conformation for 50 key regulatory loci across five individuals will be assessed by 5C. These data will be used to further refine our regulatory model. The result of this analysis will be a model that highlights the key transcription factors and cis-acting components that drive gene expression in LPS-stimulated human DCs. Our findings are expected to provide a more general method for identifying the genetic determinants of gene expression in primary mammalian cells.

Public Health Relevance

The proposed project will develop models for predicting whether a given gene will be expressed, under a given condition, based on the primary sequence of the gene and other genetic elements that regulate it. Models will be based on genetic features of the human dendritic cell - the key white blood cell that orchestrates the immune response - as these dynamic cells undergo a developmental switch in response to detection of pathogens. The experiments proposed here are expected to teach us fundamental biology of critical relevance to the development of vaccines and the prevention of autoimmunity, as well as define the more globally important grammar of gene regulation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01HG007910-03
Application #
9177761
Study Section
Special Emphasis Panel (ZHG1)
Program Officer
Pazin, Michael J
Project Start
2015-01-05
Project End
2017-11-30
Budget Start
2016-12-01
Budget End
2017-11-30
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Massachusetts Medical School Worcester
Department
Other Basic Sciences
Type
Schools of Medicine
DUNS #
603847393
City
Worcester
State
MA
Country
United States
Zip Code
01655
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DeTomaso, David; Yosef, Nir (2016) FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data. BMC Bioinformatics 17:315
Valton, Anne-Laure; Dekker, Job (2016) TAD disruption as oncogenic driver. Curr Opin Genet Dev 36:34-40
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

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