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Dryad

Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer

Cite this dataset

Kim, Yang Joon et al. (2023). Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer [Dataset]. Dryad. https://doi.org/10.5061/dryad.7sqv9s4sv

Abstract

A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity capture the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.

Methods

Data were collected using fluorescent confocal microscopy as described in the Methods and Materials section of the main text, and processed using the image processing techniques described therein. The file 'compiledData.mat' represents the pre-and post-processed transcription data using the fitting schemes described in the main text. Data for the concentration of input transcription factors, Bicoid and Runt, are stored in 'Bicoid.mat' and 'Runt.mat' respectively.

Usage notes

Data files are in MATLAB .mat file structures.

Funding

Burroughs Wellcome Fund, Award: Career Award at the Scientific Interface

National Cancer Institute, Award: P20 GM0103423

National Cancer Institute, Award: Director's New Innovator Award DP2 OD024541-01

National Science Foundation, Award: CAREER Award 1652236

National Science Foundation, Award: CAREER Award 1349779

Alfred P. Sloan Foundation, Award: Sloan Research Fellowship

Kinship Conservation Fellows

Shurl and Kay Curci Foundation

Hellman Foundation

Korea Foundation for Advanced Studies