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UMass-led Team Exploring Dendritic Cells as Model for Gene Expression Research


NEW YORK (GenomeWeb) – A three-year, $6.1 million grant from the National Institutes of Health will fund a project to build predictive models of gene expression using human dendritic cells, according to a recently published grant abstract.

Scientists led by Jeremy Luban and Manuel Garber of the University of Massachusetts Medical School will use a range of genomic technologies to accomplish this, including RNA sequencing, CRISPR/Cas9 editing, ChIP-sequencing, and ATAC-sequencing.

"This grant is aimed at building models in which you should be able to predict what a gene expression pattern would be, given a particular DNA sequence," Garber told GenomeWeb. The project is driven by fundamental questions about the language of gene expression.

"What is the consequence of a mutation? Is a key cytokine expressed at too high a level, or not long enough, or for too long, or at the wrong time? It's currently very hard to tell, even if you know that a given mutation affects the binding of a transcription factor," Garber said.  "In [the] long term, via our study, a model will allow us to determine exactly what happens when you have anomalous binding of a transcription factor."

The scientists expect that their investigation into the transcription factors and cis-regulatory components driving gene expression in dendritic cells can be expanded to provide a more general explanation for what drives gene expression in other cells.

The role of dendritic cells in the body lends them certain advantages as potential models of gene expression. Their function in the immune system is to present antigens to T cells, so the transcriptional regulatory network that underlies pathogen detection is easily stimulated. "Because they're stimulated in coordinated ways, they provide a way to test the functionality of cis-regulatory elements," Garber said.

Dendritic cells also express genes very rapidly, saving time. The first wave of expression begins half an hour after they're stimulated, Garber explained. A second wave of expression comes after one hour, and a third after two hours. "The whole of the main gene expression ends at six hours," Garber said, which is fast compared to many other types of cells.

The first step is to figure out what the genes are doing. "We should be able to tell what genes will do and how the transcription factors are assembled," Garber said. "This is the foundation upon which you'll eventually be able to predict the expression of particular genes." To do this, the scientists will use RNA-seq to find the genes that respond to stimulation, and ChIP-seq to map chromatin features of these genes.

The next step is to find what the cis-regulatory elements are and where they are in the genome, also using RNA-seq. The functional data for each transcription factor will be complemented by measuring protein-DNA interactions using ChIP-seq. By pinpointing key transcription factors, their binding sites, and the genes that respond to them, the scientists can refine their model of the gene network. They will use ATAC-seq to find which regions of DNA are open for binding within each enhancer.

"Once we build an observational model, we'll want to perturb it to test the predictions of the model," Garber said. To do this, they will use CRISPR/Cas9 to selectively take out regulatory elements to find the downstream effects on gene expression.

The array of technologies used, which Garber said is becoming more common for studies like this, means that he and Luban have several collaborators who will perform tasks such as data integration and CRISPR/Cas9 editing.

Besides the main goal of understanding the language of gene expression, the research could specifically inform the development of vaccines and the condition of autoimmunity.