The chief aim of this grant is to establish a strategy to study complete transcription networks in animals. A fundamental challenge in the """"""""post genome era"""""""" is to decipher the transcriptional information contained in the extensive cis-acting DNA sequences that direct intricate patterns of gene expression in complex organisms. We argue that these challenges cannot be fully met without a systematic characterization of all the components of the transcription regulatory network. The transcriptional network in the early Drosophila embryo is uniquely suited for such system-wide analyses because it offers powerful molecular and genetic tools, is relatively simple, and contains a known, tractable number of regulators. Using this system, we propose to develop methods and strategies to collect four essential classes of data and to use these data to develop bioinformatic analyses that predict which regulatory sequences transcription factors bind in vivo, the combinatorial code determining how factors interact once bound to promoters, and the patterns of expression driven by particular regulatory sequences. Throughout the project, the data collection methods will be refined and modified in response to our analysis of the data.
Our specific aims are to: ? ? 1. Develop a strategy to obtain a new, more detailed understanding of the in vitro DNA binding specificities of transcription factors using a modified binding site selection protocol and other methods. ? ? 2. Optimize in vivo crosslinking and genomic microarrays to measure binding of endogenous factors to thousands of DNA elements in living embryos. ? ? 3. Develop advanced imaging methods to quantitate gene expression patterns in 3D with single cell resolution and use this information to identify transcription factor target genes. ? ? 4. Explore a novel transgenic promoter based strategy to test large numbers of predicted functional cis-regulatory sequences and determine the expression patterns they drive to aid future predictions. ? ? 5. Use engineering solutions to increase the throughout of Aims 1-4 to enable comprehensive analysis of not only the early Drosophila network, but also larger networks in other animals, including mammals. ? ? 6. Develop bioinformatic tools that utilize the data in Aims 1-4 to analyze the transcriptional network, and make the data and algorithms available to the community at large. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM070444-03
Application #
6924729
Study Section
Special Emphasis Panel (ZHG1-HGR-N (J1))
Program Officer
Tompkins, Laurie
Project Start
2003-08-01
Project End
2008-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
3
Fiscal Year
2005
Total Cost
$2,898,395
Indirect Cost
Name
Lawrence Berkeley National Laboratory
Department
Genetics
Type
Organized Research Units
DUNS #
078576738
City
Berkeley
State
CA
Country
United States
Zip Code
94720
Martinez, Carlos; Rest, Joshua S; Kim, Ah-Ram et al. (2014) Ancestral resurrection of the Drosophila S2E enhancer reveals accessible evolutionary paths through compensatory change. Mol Biol Evol 31:903-16
Kim, Ah-Ram; Martinez, Carlos; Ionides, John et al. (2013) Rearrangements of 2.5 kilobases of noncoding DNA from the Drosophila even-skipped locus define predictive rules of genomic cis-regulatory logic. PLoS Genet 9:e1003243
Martinez, Carlos A; Barr, Kenneth A; Kim, Ah-Ram et al. (2013) A synthetic biology approach to the development of transcriptional regulatory models and custom enhancer design. Methods 62:91-8
Li, Xiao-Yong; Thomas, Sean; Sabo, Peter J et al. (2011) The role of chromatin accessibility in directing the widespread, overlapping patterns of Drosophila transcription factor binding. Genome Biol 12:R34
Kaplan, Tommy; Li, Xiao-Yong; Sabo, Peter J et al. (2011) Quantitative models of the mechanisms that control genome-wide patterns of transcription factor binding during early Drosophila development. PLoS Genet 7:e1001290
Fowlkes, Charless C; Eckenrode, Kelly B; Bragdon, Meghan D et al. (2011) A conserved developmental patterning network produces quantitatively different output in multiple species of Drosophila. PLoS Genet 7:e1002346
Bradley, Robert K; Li, Xiao-Yong; Trapnell, Cole et al. (2010) Binding site turnover produces pervasive quantitative changes in transcription factor binding between closely related Drosophila species. PLoS Biol 8:e1000343
Rübel, Oliver; Ahern, Sean; Bethel, E Wes et al. (2010) Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data. Procedia Comput Sci 1:1757-1764
Aswani, Anil; Keränen, Soile V E; Brown, James et al. (2010) Nonparametric identification of regulatory interactions from spatial and temporal gene expression data. BMC Bioinformatics 11:413
Rübel, Oliver; Weber, Gunther H; Huang, Min-Yu et al. (2010) Integrating data clustering and visualization for the analysis of 3D gene expression data. IEEE/ACM Trans Comput Biol Bioinform 7:64-79

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