With the increasing availability of completely sequenced genomes and technologies for measuring the expression of thousands of genes, there is now increasing optimism that understanding transcriptional network dynamics can become a predictive discipline. Just as we can routinely predict the function of novel genes by sequence homology, it would be desirable to predict the context-dependent transcriptional activity of a gene from the DNA sequence features within its regulatory (non-protein coding) region. We propose a comprehensive inter-disciplinary research program, aimed at establishing the above paradigm. Accordingly, our specific aims are to: (1) use Bayesian networks to learn causal relationships between cis-regulatory motifs and gene expression patterns; (2) use inter-species conservation to identify cis-regulatory motifs, learn their functional constraints, and model their evolution; (3) extend specific aims 1 and 2 to the study of transcription in metazoan genomes; (4) apply a high-throughput phage-display selection strategy to identify transcription factors which bind computationally predicted cis-regulatory motifs. If implemented, the proposed research program will significantly enhance our understanding of transcriptional network structure and dynamics. On a practical level, this knowledge will set the foundation for engineering of custom regulatory circuits and rational interventions to affect human disease processes.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG003219-03
Application #
7076194
Study Section
Special Emphasis Panel (ZRG1-SSS-G (92))
Program Officer
Bonazzi, Vivien
Project Start
2004-09-01
Project End
2008-05-31
Budget Start
2006-06-01
Budget End
2007-05-31
Support Year
3
Fiscal Year
2006
Total Cost
$342,514
Indirect Cost
Name
Princeton University
Department
Type
Organized Research Units
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
Alarcón, Claudio R; Goodarzi, Hani; Lee, Hyeseung et al. (2015) HNRNPA2B1 Is a Mediator of m(6)A-Dependent Nuclear RNA Processing Events. Cell 162:1299-308
Oikonomou, Panos; Goodarzi, Hani; Tavazoie, Saeed (2014) Systematic identification of regulatory elements in conserved 3' UTRs of human transcripts. Cell Rep 7:281-92
Goodarzi, Hani; Zhang, Steven; Buss, Colin G et al. (2014) Metastasis-suppressor transcript destabilization through TARBP2 binding of mRNA hairpins. Nature 513:256-60
Chiu, Isaac M; Morimoto, Emiko T A; Goodarzi, Hani et al. (2013) A neurodegeneration-specific gene-expression signature of acutely isolated microglia from an amyotrophic lateral sclerosis mouse model. Cell Rep 4:385-401
Goodarzi, Hani; Najafabadi, Hamed S; Oikonomou, Panos et al. (2012) Systematic discovery of structural elements governing stability of mammalian messenger RNAs. Nature 485:264-8
Birsoy, Kivanç; Berry, Ryan; Wang, Tim et al. (2011) Analysis of gene networks in white adipose tissue development reveals a role for ETS2 in adipogenesis. Development 138:4709-19
Lieber, Daniel S; Elemento, Olivier; Tavazoie, Saeed (2010) Large-scale discovery and characterization of protein regulatory motifs in eukaryotes. PLoS One 5:e14444
Goodarzi, Hani; Elemento, Olivier; Tavazoie, Saeed (2009) Revealing global regulatory perturbations across human cancers. Mol Cell 36:900-11
Freckleton, Gordon; Lippman, Soyeon I; Broach, James R et al. (2009) Microarray profiling of phage-display selections for rapid mapping of transcription factor-DNA interactions. PLoS Genet 5:e1000449
Legesse-Miller, Aster; Elemento, Olivier; Pfau, Sarah J et al. (2009) let-7 Overexpression leads to an increased fraction of cells in G2/M, direct down-regulation of Cdc34, and stabilization of Wee1 kinase in primary fibroblasts. J Biol Chem 284:6605-9

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