It has long been puzzling how a relatively small number of transcription factors (TFs) can precisely control expression of ~21,000 ORFs and probably 10-fold more non-coding RNAs in humans. Another major gap in the transcription field is a lack of a simple set of rules that explain the specificity of protein-DNA interactions. These gaps represent a major problem because, until they are filled, understanding of the transcription circuitry and its underlining principles will remain highly incomplete. The long-term goals are to characterize the human protein-DNA interaction (PDI) network and elucidate the underlining molecular mechanisms of transcriptional regulation using the combined force of protein microarray technologies and bioinformatics. The objectives of this particular application are to identify a comprehensive list of sequence-specific unconventional DNA-binding proteins (uDBPs) and to better define rules that specify TF-DNA interactions. The central hypothesis is that unbiased, high-throughput profiling of PDIs will reveal rules and organization of transcriptional regulatory networks and pathways. This hypothesis has been formulated on the basis of preliminary data produced in the applicants

Public Health Relevance

The propose research is relevant to public health because generation of a comprehensive list of participants in human transcriptional control and regulation and characterization of the rules that define DNA-binding specificity is ultimately expected to increase understanding of diseases that result from defects in transcription factor function. The proposed research is thus relevant to part of the NIH's mission as this advance in fundamental biological knowledge will help identify better therapeutics for a broad range of human diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM076102-05A1
Application #
8106745
Study Section
Special Emphasis Panel (ZRG1-GGG-F (02))
Program Officer
Tompkins, Laurie
Project Start
2006-07-04
Project End
2015-03-31
Budget Start
2011-04-01
Budget End
2012-03-31
Support Year
5
Fiscal Year
2011
Total Cost
$399,696
Indirect Cost
Name
Johns Hopkins University
Department
Pharmacology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Woodard, Crystal; Liao, Gangling; Goodwin, C Rory et al. (2015) A Screen for Extracellular Signal-Regulated Kinase-Primed Glycogen Synthase Kinase 3 Substrates Identifies the p53 Inhibitor iASPP. J Virol 89:9232-41
Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin et al. (2015) Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction. PLoS Comput Biol 11:e1004508
Coelho, Paulo S R; Im, Hogune; Clemons, Karl V et al. (2015) Evaluating Common Humoral Responses against Fungal Infections with Yeast Protein Microarrays. J Proteome Res 14:3924-31
Tu, Shun; Guo, Shu-Juan; Chen, Chien-Sheng et al. (2015) YcgC represents a new protein deacetylase family in prokaryotes. Elife 4:
Lee, Yun-Il; Giovinazzo, Daniel; Kang, Ho Chul et al. (2014) Protein microarray characterization of the S-nitrosoproteome. Mol Cell Proteomics 13:63-72
Hu, Jianfei; Rho, Hee-Sool; Newman, Robert H et al. (2014) PhosphoNetworks: a database for human phosphorylation networks. Bioinformatics 30:141-2
Hu, Jianfei; Rho, Hee-Sool; Newman, Robert H et al. (2014) Global analysis of phosphorylation networks in humans. Biochim Biophys Acta 1844:224-31
Fan, Baochang; Lu, Kuan-Yi; Reymond Sutandy, F X et al. (2014) A human proteome microarray identifies that the heterogeneous nuclear ribonucleoprotein K (hnRNP K) recognizes the 5' terminal sequence of the hepatitis C virus RNA. Mol Cell Proteomics 13:84-92
Guo, Junjie U; Su, Yijing; Shin, Joo Heon et al. (2014) Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain. Nat Neurosci 17:215-22
Woodard, Crystal L; Goodwin, C Rory; Wan, Jun et al. (2013) Profiling the dynamics of a human phosphorylome reveals new components in HGF/c-Met signaling. PLoS One 8:e72671

Showing the most recent 10 out of 32 publications