Sequence-specific transcription factors (TFs) regulate gene expression through their interactions with DNA sequences in the genome. The goals of this project are to continue developing approaches and data sets for understanding the DNA binding specificities of TFs and to identify the effects of coding polymorphisms within TFs'DNA binding domains on their DNA binding preferences. Identification of TFs'DNA binding specificities is important in understanding transcriptional regulatory networks, in particular in the prediction of cis regulator modules, inference of cis regulatory codes, and interpretation of in vivo TF binding data and gene expression data. Identification of the DNA binding effects of such polymorphic TF variants will be essential in studies aimed at understanding the gene regulatory effects resulting from natural genetic variation. This project will focus on human TFs, with an emphasis on those with known mutations or polymorphisms identified in exome or whole-genome sequencing projects. Our results will provide data that will likely be of importance to other systems, and more generally, our data, approaches, technologies, and database will be useful not only for human TFs but also for model organism studies. Specifically, we will: (1) develop a computational pipeline to predict the effects of coding mutations or polymorphisms within TF DNA binding domains (DBDs) for TFs of major structural classes;(2) determine the DNA binding specificities of mutant TFs designed to test our computational pipeline;(3) experimentally determine the effects of known mutations or coding polymorphisms within human TF DBDs;(4) further develop and maintain the UniPROBE database of universal protein binding microarray (PBM) data on TFs'DNA binding specificities.

Public Health Relevance

The interactions between transcription factors (TFs) and their DNA binding sites are an integral part of gene regulatory networks within cells;however, it is not well understood how mutations or polymorphisms within TFs affect their DNA binding activities. In this project, we will develop a computational pipeline to identify with greater accuracy potentially damaging mutations or polymorphisms within TFs, and we will test such predictions experimentally. The resulting data are anticipated to improve the ability to understand the potential effects of such TF mutations or polymorphisms on gene regulation.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG003985-08
Application #
8710309
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Smith, Michael
Project Start
2006-07-26
Project End
2016-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
8
Fiscal Year
2014
Total Cost
$579,028
Indirect Cost
$229,507
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Mariani, Luca; Weinand, Kathryn; Vedenko, Anastasia et al. (2017) Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds. Cell Syst 5:187-201.e7
Hafner, Antonina; Stewart-Ornstein, Jacob; Purvis, Jeremy E et al. (2017) p53 pulses lead to distinct patterns of gene expression albeit similar DNA-binding dynamics. Nat Struct Mol Biol 24:840-847
Inukai, Sachi; Kock, Kian Hong; Bulyk, Martha L (2017) Transcription factor-DNA binding: beyond binding site motifs. Curr Opin Genet Dev 43:110-119
Nelms, Bradlee D; Waldron, Levi; Barrera, Luis A et al. (2016) CellMapper: rapid and accurate inference of gene expression in difficult-to-isolate cell types. Genome Biol 17:201
Barrera, Luis A; Vedenko, Anastasia; Kurland, Jesse V et al. (2016) Survey of variation in human transcription factors reveals prevalent DNA binding changes. Science 351:1450-1454
Hume, Maxwell A; Barrera, Luis A; Gisselbrecht, Stephen S et al. (2015) UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein-DNA interactions. Nucleic Acids Res 43:D117-22
Nishi, Yuichi; Zhang, Xiaoxiao; Jeong, Jieun et al. (2015) A direct fate exclusion mechanism by Sonic hedgehog-regulated transcriptional repressors. Development 142:3286-93
Menke, Chelsea; Cionni, Megan; Siggers, Trevor et al. (2015) Grhl2 is required in nonneural tissues for neural progenitor survival and forebrain development. Genesis :
Siggers, Trevor; Reddy, Jessica; Barron, Brian et al. (2014) Diversification of transcription factor paralogs via noncanonical modularity in C2H2 zinc finger DNA binding. Mol Cell 55:640-8
Christodoulou, Danos C; Wakimoto, Hiroko; Onoue, Kenji et al. (2014) 5'RNA-Seq identifies Fhl1 as a genetic modifier in cardiomyopathy. J Clin Invest 124:1364-70

Showing the most recent 10 out of 48 publications