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      Multimodal transcriptional control of pattern formation in embryonic development

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Significance

          Predicting how the gene expression patterns that specify animal body plans arise from interactions between transcription factor proteins and regulatory DNA remains a major challenge in physical biology. We utilize live imaging and theoretical approaches to examine how transcriptional control at the single-cell level gives rise to a sharp stripe of cytoplasmic mRNA in the fruit fly embryo. While the modulation of transcriptional bursting has been implicated as the primary lever for controlling gene expression, we find that this alone cannot quantitatively recapitulate pattern formation. Instead, we find that the pattern arises through the joint action of 2 regulatory strategies—control of bursting and control of the total duration of transcription—that originate from distinct underlying molecular mechanisms.

          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.

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          Most cited references 80

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          Localization of ASH1 mRNA particles in living yeast.

          ASH1 mRNA localizes to the bud tip in Saccharomyces cerevisiae to establish asymmetry of HO expression, important for mating type switching. To visualize real time localization of the mRNA in living yeast cells, green fluorescent protein (GFP) was fused to the RNA-binding protein MS2 to follow a reporter mRNA containing MS2-binding sites. Formation and localization of a GFP particle in the bud required ASH1 3'UTR (untranslated region) sequences. The SHE mutants disrupt RNA and particle localization and SHE 2 and 3 mutants inhibit particle formation as well. Both She3myc and She1myc colocalized with the particle. Video microscopy demonstrated that She1p/Myo4p moved particles to the bud tip at 200-440 nm/sec. Therefore, the ASH1 3'UTR-dependent particle serves as a marker for RNA transport and localization.
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            Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

            Transcriptional regulation is an inherently noisy process. The origins of this stochastic behavior can be traced to the random transitions among the discrete chemical states of operators that control the transcription rate and to finite number fluctuations in the biochemical reactions for the synthesis and degradation of transcripts. We develop stochastic models to which these random reactions are intrinsic and a series of simpler models derived explicitly from the first as approximations in different parameter regimes. This innate stochasticity can have both a quantitative and qualitative impact on the behavior of gene-regulatory networks. We introduce a natural generalization of deterministic bifurcations for classification of stochastic systems and show that simple noisy genetic switches have rich bifurcation structures; among them, bifurcations driven solely by changing the rate of operator fluctuations even as the underlying deterministic system remains unchanged. We find stochastic bistability where the deterministic equations predict monostability and vice-versa. We derive and solve equations for the mean waiting times for spontaneous transitions between quasistable states in these switches.
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              Mammalian genes are transcribed with widely different bursting kinetics.

              In prokaryotes and eukaryotes, most genes appear to be transcribed during short periods called transcriptional bursts, interspersed by silent intervals. We describe how such bursts generate gene-specific temporal patterns of messenger RNA (mRNA) synthesis in mammalian cells. To monitor transcription at high temporal resolution, we established various gene trap cell lines and transgenic cell lines expressing a short-lived luciferase protein from an unstable mRNA, and recorded bioluminescence in real time in single cells. Mathematical modeling identified gene-specific on- and off-switching rates in transcriptional activity and mean numbers of mRNAs produced during the bursts. Transcriptional kinetics were markedly altered by cis-regulatory DNA elements. Our analysis demonstrated that bursting kinetics are highly gene-specific, reflecting refractory periods during which genes stay inactive for a certain time before switching on again.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                14 January 2020
                27 December 2019
                27 December 2019
                : 117
                : 2
                : 836-847
                Affiliations
                aBiophysics Graduate Group, University of California, Berkeley, CA 94720;
                bBiochemistry and Molecular Biophysics Option, California Institute of Technology, Pasadena, CA 91126;
                cDepartment of Physics, Columbia University, New York, NY 10027;
                dDepartment of Physics, University of California, Berkeley, CA 94720;
                eDepartment of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10027;
                fData Science Institute, Columbia University, New York, NY 10027;
                gDepartment of Systems Biology, Columbia University, New York, NY 10027;
                hDepartment of Statistics, Columbia University, New York, NY 10027;
                iDepartment of Molecular and Cell Biology, University of California, Berkeley, CA 94720;
                jInstitute for Quantitative Biosciences-QB3, University of California, Berkeley, CA 94720
                Author notes
                2To whom correspondence may be addressed. Email: hggarcia@ 123456berkeley.edu or chris.wiggins@ 123456columbia.edu .

                Edited by Michael Levine, Princeton University, Princeton, NJ, and approved November 26, 2019 (received for review July 19, 2019)

                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.

                1N.C.L. and V.G. contributed equally to this work.

                Article
                201912500
                10.1073/pnas.1912500117
                6969519
                31882445
                Copyright © 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).

                Page count
                Pages: 12
                Product
                Funding
                Funded by: HHS | NIH | NIH Office of the Director (OD) 100000052
                Award ID: DP2 OD024541-01
                Award Recipient : Nicholas C. Lammers Award Recipient : Hernan G Garcia
                Funded by: Alfred P. Sloan Foundation 100000879
                Award ID: NA
                Award Recipient : Hernan G Garcia
                Funded by: Human Frontier Science Program (HFSP) 100004412
                Award ID: NA
                Award Recipient : Hernan G Garcia
                Funded by: Searle Scholers Program
                Award ID: NA
                Award Recipient : Hernan G Garcia
                Funded by: Hellman Foundation 100010336
                Award ID: NA
                Award Recipient : Hernan G Garcia
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: IIS-1344668
                Award Recipient : Chris H Wiggins Award Recipient : Hernan G Garcia
                Funded by: HHS | NIH | NIH Office of the Director (OD) 100000052
                Award ID: DP2 OD024541-01
                Award Recipient : Nicholas C. Lammers Award Recipient : Hernan G Garcia
                Funded by: Research Foundation of The City University of New York (RFCUNY) 100004870
                Award ID: RFCUNY 40D14-A
                Award Recipient : Chris H Wiggins
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: 1652236
                Award Recipient : Chris H Wiggins Award Recipient : Hernan G Garcia
                Categories
                Physical Sciences
                Biophysics and Computational Biology
                Biological Sciences
                Developmental Biology

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