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| Status |
Public on Apr 03, 2018 |
| Title |
Divergence in DNA specificity among paralogous transcription factors contributes to their differential in vivo binding [RG_Elk1Ets1Gabpa_v1] |
| Organism |
Homo sapiens |
| Experiment type |
Genome binding/occupancy profiling by array
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| Summary |
Members of transcription factor (TF) families, i.e. paralogous TFs, are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions in the cell. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein cofactors or the chromatin environment. Contrary to previous assumptions, we find that paralogous TFs have different intrinsic preferences for DNA, not captured by current motif models, and these differences partly explain differential genomic binding and functional specificity. Our finding was possible due to a unique combination of carefully designed high-throughput assays and rigorous computation modeling, integrated into a unified framework called iMADS. We used iMADS to quantity, model, and analyze specificity differences between 11 paralogous TFs from 4 distinct human TF families. Our finding of differential specificity between closely related TFs has important implications for the interpretation of the regulatory effects of non-coding genetic variants.
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| Overall design |
Four genomic-context protein binding microarray (gcPBM) experiments were performed for recombinant, full-length, human transcription factors Elk1, Ets1, and Gabpa. Briefly, the PBMs involved binding of GST-tagged transcription factors Elk1, Ets1, and Gabpa to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Each genomic DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot.
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| Contributor(s) |
Gordan R |
| Citation(s) |
29605182 |
| NIH grant(s) |
| Grant ID |
Grant title |
Affiliation |
Name |
| R01 GM117106 |
New methods for quantitative modeling of protein-DNA interactions |
DUKE UNIVERSITY |
Raluca Gordan |
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| Submission date |
Apr 14, 2017 |
| Last update date |
Jul 18, 2018 |
| Contact name |
Raluca Gordan |
| E-mail(s) |
raluca.gordan@duke.edu
|
| Organization name |
Duke University
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| Department |
Center for Genomic and Computational Biology
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| Street address |
101 Science Dr, CIEMAS 2179
|
| City |
Durham |
| State/province |
NC |
| ZIP/Postal code |
27708 |
| Country |
USA |
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| Platforms (1) |
| GPL23305 |
Duke/RG_Elk1Ets1Gabpa_v1 (Agilent 4x180k) |
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| Samples (4)
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| This SubSeries is part of SuperSeries: |
| GSE97794 |
Divergence in DNA specificity among paralogous transcription factors contributes to their differential in vivo binding |
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| Relations |
| BioProject |
PRJNA382895 |
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