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Published February 4, 2019 | Submitted + Published + Supplemental Material
Journal Article Open

Mapping DNA sequence to transcription factor binding energy in vivo

Abstract

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site's DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 k_BT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor's sequence specificity.

Additional Information

© 2019 Barnes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: May 22, 2018; Accepted: November 6, 2018; Published: February 4, 2019. Data Availability Statement: All data was collected, stored, and preserved using the Git version control software in combination with offsite storage and hosting website GitHub. Code used to generate all figures and perform processing and analyses is available on the GitHub repository (https://www.github.com/rpgroup-pboc/seq_mapping). Inferred model parameters for each energy weight matrix are also available here. Raw flow cytometry data files (.fcs and .csv) files were stored on-site under redundant storage. Raw flow cytometry data files (.fcs and .csv) files are available on the CaltechDATA repository (DOI: 10.22002/D1.1108). Sequencing data is available through the NCBI website under accession number SRP146291. Funding: SLB, NMB, WTI, and RP received funding from the National Institutes of Health Director's Pioneer Award DP1 OD000217 https://commonfund.nih.gov/pioneer) and the National Institutes of Health Maximizing Investigators' Research Award 1R35 GM118043-01 (https://www.nigms.nih.gov/Research/mechanisms/MIRA/Pages/default.aspx). SLB received funding from the National Science Foundation Graduate Research Fellowship Program (https://www.nsfgrfp.org/). NMB received funding from the Howard Hughes Medical Institute International Student Fellowship (https://www.hhmi.org/developing-scientists/international-student-research-fellowships). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. Access to the Miltenyi Biotec MACSquant Analyzer 10 Flow Cytometer was graciously provided by the Pamela Björkman lab at Caltech. Access to the Beckman-Coulter MoFlo XDP cell sorter was provided by the Tirrell lab, with ongoing technical support from Tirrell lab members Seth Lieblich and Bradley Silverman. Manuel Razo and Griffin Chure from the Phillips lab assisted with constructing the LacI amino acid mutants. Suzannah Beeler from the Phillips lab provided helpful comments on the manuscript draft. Author Contributions: Conceptualization: Stephanie L. Barnes, Nathan M. Belliveau, Justin B. Kinney, Rob Phillips. Data curation: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland. Formal analysis: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland. Funding acquisition: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland, Rob Phillips. Investigation: Stephanie L. Barnes, Nathan M. Belliveau. Methodology: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland, Justin B. Kinney. Project administration: Stephanie L. Barnes. Resources: Justin B. Kinney, Rob Phillips. Software: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland, Justin B. Kinney. Supervision: Rob Phillips. Validation: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland. Visualization: Stephanie L. Barnes. Writing – original draft: Stephanie L. Barnes, Nathan M. Belliveau. Writing – review & editing: Stephanie L. Barnes, Nathan M. Belliveau, William T. Ireland, Justin B. Kinney, Rob Phillips.

Attached Files

Published - journal.pcbi.1006226.pdf

Submitted - 331124.full.pdf

Supplemental Material - journal.pcbi.1006226.s001.pdf

Supplemental Material - journal.pcbi.1006226.s002.pdf

Supplemental Material - journal.pcbi.1006226.s003.pdf

Supplemental Material - journal.pcbi.1006226.s004.pdf

Supplemental Material - journal.pcbi.1006226.s005.pdf

Supplemental Material - journal.pcbi.1006226.s006.pdf

Supplemental Material - journal.pcbi.1006226.s007.pdf

Supplemental Material - journal.pcbi.1006226.s008.pdf

Supplemental Material - journal.pcbi.1006226.s009.pdf

Supplemental Material - journal.pcbi.1006226.s010.pdf

Supplemental Material - journal.pcbi.1006226.s011.pdf

Supplemental Material - journal.pcbi.1006226.s012.pdf

Supplemental Material - journal.pcbi.1006226.s013.tar.gz

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Additional details

Created:
August 19, 2023
Modified:
October 20, 2023