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Multimodal Transcriptional Control of Pattern Formation in Embryonic Development

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Multimodal Transcriptional Control of Pattern Formation in Embryonic Development Multimodal transcriptional control of pattern formation in embryonic development Nicholas C. Lammersa,1 , Vahe Galstyanb,c,1, Armando Reimera, Sean A. Medind, Chris H. Wigginse,f,g,h,2, and Hernan G. Garciaa,d,i,j,2 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; and jInstitute for Quantitative Biosciences- QB3, University of California, Berkeley, CA 94720 Edited by Michael Levine, Princeton University, Princeton, NJ, and approved November 26, 2019 (received for review July 19, 2019) Predicting how interactions between transcription factors and stripe transcribe at a higher average rate than nuclei on the stripe regulatory DNA sequence dictate rates of transcription and, ulti- boundaries (Fig. 1B). We identify this graded transcriptional mately, drive developmental outcomes remains an open challenge control strategy with the analog control of gene expression. in physical biology. Using stripe 2 of the even-skipped gene Alternatively, transcription factors could exert control over the in Drosophila embryos as a case study, we dissect the regula- length of time a nucleus is transcriptionally active (Fig. 1C). In tory forces underpinning a key step along the developmental this binary control scheme—akin to an on/off switch that dic- decision-making cascade: the generation of cytoplasmic mRNA tates whether a nucleus is transcriptionally active or quiescent— patterns via the control of transcription in individual cells. Using individual nuclei transcribe at the same average rate regardless live imaging and computational approaches, we found that the of their position along the stripe, but for different lengths of time. transcriptional burst frequency is modulated across the stripe to Finally, some nuclei might not engage in transcription at all dur- control the mRNA production rate. However, we discovered that ing the formation of the pattern (Fig. 1D). Here, a larger fraction bursting alone cannot quantitatively recapitulate the formation of nuclei engage in mRNA production in the stripe center than of the stripe and that control of the window of time over which in the boundaries. Any of these scenarios, or some combina- each nucleus transcribes even-skipped plays a critical role in stripe tion thereof, can explain the formation of a cytoplasmic mRNA formation. Theoretical modeling revealed that these regulatory pattern. strategies (bursting and the time window) respond in different To quantify the contribution of each regulatory strategy to pat- ways to input transcription factor concentrations, suggesting that tern formation and thereby move toward a deeper understanding the stripe is shaped by the interplay of 2 distinct underlying of the molecular processes at play, it is necessary to measure molecular processes. Significance transcriptional bursting j gene regulation j development j hidden Markov models Predicting how the gene expression patterns that specify ani- mal body plans arise from interactions between transcription uring embryonic development, tightly choreographed pat- factor proteins and regulatory DNA remains a major challenge Dterns of gene expression—shallow gradients, sharp steps, in physical biology. We utilize live imaging and theoretical narrow stripes—specify cell fates. The correct positioning, sharp- approaches to examine how transcriptional control at the ness, and amplitude of these patterns of cytoplasmic mRNA and single-cell level gives rise to a sharp stripe of cytoplasmic protein ensure the reliable determination of animal body plans mRNA in the fruit fly embryo. While the modulation of tran- (1). Yet, despite decades of work mapping the gene regulatory scriptional bursting has been implicated as the primary lever networks that drive development, and despite extensive efforts to for controlling gene expression, we find that this alone can- dissect the regulatory logic of the enhancer elements that dictate not quantitatively recapitulate pattern formation. Instead, we the behavior of these networks, a predictive understanding of find that the pattern arises through the joint action of 2 how gene expression patterns and developmental outcomes are regulatory strategies—control of bursting and control of the driven by transcription factor concentrations remains a central total duration of transcription—that originate from distinct challenge in the field (2). underlying molecular mechanisms. Predicting developmental outcomes demands a quantitative understanding of the flow of information along the central Author contributions: C.H.W. and H.G.G. designed research; N.C.L., V.G., and H.G.G. per- dogma: how input transcription factors dictate the output rate formed 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 of mRNA production, how this rate of mRNA production dic- the paper.y tates cytoplasmic patterns of mRNA, and how these mRNA The authors declare no competing interest.y patterns lead to protein patterns that feed back into the gene regulatory network. While the connection between transcrip- This article is a PNAS Direct Submission.y This open access article is distributed under Creative Commons Attribution-NonCommercial- tion factor concentration and output mRNA production rate has NoDerivatives License 4.0 (CC BY-NC-ND).y been the subject of active research over the last 3 decades (2– Data deposition: All data sources used in this work can be found in the project’s 11), the connection between this output rate and the resulting GitHub repository at https://github.com/GarciaLab/cpHMM/tree/master/multimodal cytoplasmic patterns of mRNA has remained largely unexplored. control paper sandbox/dat.y For example, a graded stripe of cytoplasmic mRNA within an 1 N.C.L. and V.G. contributed equally to this work.y embryo could arise as a result of radically different transcrip- 2 To whom correspondence may be addressed. Email: [email protected] or chris. tional dynamics at the single-nucleus level (Fig. 1A). Specifically, [email protected] if individual nuclei along this stripe modulate their average RNA This article contains supporting information online at https://www.pnas.org/lookup/suppl/ polymerase (RNAP) loading rate, then graded control of the doi:10.1073/pnas.1912500117/-/DCSupplemental.y mean rate of transcription results: Nuclei in the middle of the First published December 27, 2019. 836–847 j PNAS j January 14, 2020 j vol. 117 j no. 2 www.pnas.org/cgi/doi/10.1073/pnas.1912500117 Downloaded by guest on September 25, 2021 A ingly, however, we discovered that this analog control of the transcription rate is insufficient to quantitatively recapitulate the cytoplasmic mRNA stripe pattern. In addition to the control of the rate of mRNA production among nuclei, we also observed a pronounced regulation of the window of time during which eve nuclei loci were engaged in transcription across the stripe, with those cytoplasmic in the stripe center expressing for approximately 3 times longer mRNA than those in the flanks. While it is widely appreciated that genes B are transcriptionally competent for limited windows of time dur- analog control ing development, we found that—in the case of eve stripe 2—this of the mean binary transcriptionally engaged/disengaged logic is not merely a transcription rate necessary precondition for pattern formation—it is the primary driver thereof. Thus, we conclude that the regulation of eve stripe active quiescent 2 is multimodal in nature, with contributions from 3 distinct regu- C RNAP latory strategies (Fig. 1 B–D). Nonetheless, stripe formation can loading rate binary control be quantitatively explained almost entirely through the interplay of the transcriptional between 2 distinct control strategies: binary control of the dura- time window tion of transcriptional engagement (Fig. 1C) and control of the D time mean rate of transcription (Fig. 1B). control of the Building upon this result, we developed computational approaches to uncover the mechanistic underpinning of each fraction of regulatory strategy. We employed a compound-state hidden active nuclei Markov model (cpHMM) to uncover variations in transcriptional Fig. 1. Multiple modes of pattern formation by single-cell transcriptional bursting dynamics in individual nuclei across space and time activity. (A–D) Cytoplasmic mRNA patterns (A) could arise from transcription (22–24). We uncovered that, consistent with previous results, factors exerting control over the mean transcription rate (B), the transcrip- transcription factors control the rate of transcription by alter- tional time window dictating when a nucleus is transcriptionally active or ing the frequency of transcriptional bursts (25, 26). In addition, quiescent (C), or the fraction of active nuclei (D) or some combination we utilized logistic regressions to correlate eve stripe 2 transcrip-
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