Multimodal transcriptional control of pattern formation in embryonic development
Abstract
Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.
Additional Information
© 2020 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). Edited by Michael Levine, Princeton University, Princeton, NJ, and approved November 26, 2019 (received for review July 19, 2019). PNAS first published December 27, 2019. We thank Thomas Gregor and Lev Barinov for discussion about an initial implementation of the cpHMM approach; Florian Jug for help with the spot segmentation using machine learning; and Elizabeth Eck, Maryam Kazemzadeh-Atoufi, and Jonathan Liu for the hunchback P2 data used in the absolute MS2 calibration. We are also grateful to Jack Bateman, Jane Kondev, Rob Phillips, Allyson Sgro, and Donald Rio for comments and discussion on the manuscript. H.G.G. was supported by the Burroughs Wellcome Fund Career Award at the Scientific Interface, the Sloan Research Foundation, the Human Frontiers Science Program, the Searle Scholars Program, the Shurl & Kay Curci Foundation, the Hellman Foundation, the National Institutes of Health (NIH) Director's New Innovator Award (DP2 OD024541-01), and a National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) award (1652236). N.C.L. was supported by NIH Genomics and Computational Biology Training Grant 5T32HG000047-18. C.H.W. was supported by the NIH/National Cancer Institute (U54 CA193313), The City University of New York (CUNY) (RFCUNY 40D14-A), and the NSF (IIS-1344668). N.C.L. and V.G. contributed equally to this work. Author contributions: C.H.W. and H.G.G. designed research; N.C.L., V.G., and H.G.G. performed research; N.C.L., V.G., A.R., S.A.M., and C.H.W. contributed new reagents/analytic tools; N.C.L., V.G., S.A.M., and H.G.G. analyzed data; and N.C.L., V.G., and H.G.G. wrote the paper. The authors declare no competing interest. This article is a PNAS Direct Submission. Data deposition: All data sources used in this work can be found in the project's GitHub repository at https://github.com/GarciaLab/cpHMM/tree/master/multimodal_control_paper_sandbox/dat. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1912500117/-/DCSupplemental.Attached Files
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Additional details
- PMCID
- PMC6969519
- Eprint ID
- 94689
- Resolver ID
- CaltechAUTHORS:20190412-102028041
- Burroughs Wellcome Fund
- Alfred P. Sloan Foundation
- Human Frontier Science Program
- Searle Scholars Program
- Shurl and Kay Curci Foundation
- Hellman Foundation
- DP2 OD024541-01
- NIH
- PHY-1652236
- NSF
- 5T32HG000047-18
- NIH Predoctoral Fellowship
- U54 CA193313
- NIH
- RFCUNY 40D14-A
- Columbia University
- IIS-1344668
- NSF
- Created
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2019-04-12Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field