Abstract

Environmental stimuli commonly act via changes in gene regulation. Human-genome-scale assays to measure such responses are indirect or require knowledge of the transcription factors (TFs) involved. Here, we present the use of human genome-wide high-throughput reporter assays to measure environmentally-responsive regulatory element activity. We focus on responses to glucocorticoids (GCs), an important class of pharmaceuticals and a paradigmatic genomic response model. We assay GC-responsive regulatory activity across >108 unique DNA fragments, covering the human genome at >50×. Those assays directly detected thousands of GC-responsive regulatory elements genome-wide. We then validate those findings with measurements of transcription factor occupancy, histone modifications, chromatin accessibility, and gene expression. We also detect allele-specific environmental responses. Notably, the assays did not require knowledge of GC response mechanisms. Thus, this technology can be used to agnostically quantify genomic responses for which the underlying mechanism remains unknown.

Details

Title
Human genome-wide measurement of drug-responsive regulatory activity
Author
Johnson, Graham D 1   VIAFID ORCID Logo  ; Barrera, Alejandro 2 ; McDowell, Ian C 1 ; Anthony M D’Ippolito 3 ; Majoros, William H 4 ; Vockley, Christopher M 5 ; Wang, Xingyan 6 ; Allen, Andrew S 6 ; Reddy, Timothy E 7   VIAFID ORCID Logo 

1  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA 
2  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA 
3  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA 
4  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA; Computational Biology and Bioinformatics Program, Duke University, Durham, NC, USA 
5  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA 
6  Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA 
7  Center for Genomic and Computational Biology, Duke University, Durham, NC, USA; Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC, USA; University Program in Genetics and Genomics, Duke University, Durham, NC, USA; Computational Biology and Bioinformatics Program, Duke University, Durham, NC, USA 
Pages
1-9
Publication year
2018
Publication date
Dec 2018
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41467-018-07607-x
ProQuest document ID
2159700871
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.