Nuclear receptors have been proven to be successful as a druggable family of cellular regulators. We learned much of NR biology and pharmacology from very few NRs and their agonist/antagonist ligands. Among these well-studied NRs are ERa, GR, PR, AR, and - more recently - PPARg. The first four NRs mentioned account for the vast majority of NR-related peer reviewed research and essentially half of FDA-approved NR targeting therapies. There are 48 members of the NR protein family in humans, and much remains to be learned for the rest of the NRs. There are at least two ways to find and prioritize pharmaceutical opportunities for the NR class. One is to find NRs that work synergistically with or regulated by the well-studied NRs and TFs - this constitutes discovery and characterization of transcriptional crosstalk between transcriptional regulators, implying """"""""guilt of association"""""""" functions for interactors of known disease targets. The other way is to identify NRs that are activated by the major cellular signaling pathways, or NRs """"""""effectors"""""""" of a signaling transduction cascades. To enrich our understanding of the druggable genome, we propose to gather data about dynamics of NR activation and NR crosstalk with other transcription factors (TFs) under different signaling events. Our first major goal (Aim 1) is to learn from NRs with known roles, but not fully characterized mechanisms, in disease. We will use transcription factor response element pulldown (catTFRE) - a method that we recently developed for direct profiling of TF DNA binding activity to their cognate DNA response elements - to find transcriptional effectors (NRs, their coregulators, and other interacting transcription factors) of cellular response to known small molecule modulators of better-studied NRs. A complementary approach is to use catTFRE technique to find NRs, TFs, and coregulators that are activated by 7 major signaling pathways (Aim 2). This study will link signaling events with NR activation and close substantial gaps in understanding of global integrative transcriptional impact of signaling pathways.
Aim 2 data will be gathered in the context of lung cancer pathology.
Aim 3 develops informatics solutions to address issues in technical and biomedical analysis of data acquired from Aims 1 and 2. Importantly, we will develop user-friendly applications and a web portal for representation and sharing of findings from this proposal.

Public Health Relevance

Nuclear receptors have been proven to be a druggable protein family that plays important roles in transcription regulation. This project employs a newly established technical platform to profile dynamic changes in transcriptional program when cells are treated with NR agonists and antagonists. High quality data collected from proposed studies can be accessed by researchers with broad research interests, allowing for further data mining, detailed mechanistic studies and identifying new drug targets.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1)
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Yao, Yong
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Baylor College of Medicine
Schools of Medicine
United States
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Wang, Yunzhi; Song, Lei; Liu, Mingwei et al. (2018) A proteomics landscape of circadian clock in mouse liver. Nat Commun 9:1553
Oprea, Tudor I; Bologa, Cristian G; Brunak, Søren et al. (2018) Unexplored therapeutic opportunities in the human genome. Nat Rev Drug Discov 17:317-332
Ge, Sai; Xia, Xia; Ding, Chen et al. (2018) A proteomic landscape of diffuse-type gastric cancer. Nat Commun 9:1012
Zhang, Chunchao; Chen, Yue; Mao, Xinfang et al. (2017) A Bioinformatic Algorithm for Analyzing Cell Signaling Using Temporal Proteomic Data. Proteomics 17:
Zhou, Quan; Liu, Mingwei; Xia, Xia et al. (2017) A mouse tissue transcription factor atlas. Nat Commun 8:15089
Shi, Wenhao; Li, Kai; Song, Lei et al. (2016) Transcription Factor Response Elements on Tip: A Sensitive Approach for Large-Scale Endogenous Transcription Factor Quantitative Identification. Anal Chem 88:11990-11994