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      Coordinating transcription and replication to mitigate their conflicts in early Drosophila embryos

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          SUMMARY

          Collisions between transcribing RNA polymerases and DNA replication forks are disruptive. The threat of collisions is particularly acute during the rapid early embryonic cell cycles of Drosophila when S phase occupies the entirety of interphase. We hypothesize that collision-avoidance mechanisms safeguard this early transcription. Real-time imaging of endogenously tagged RNA polymerase II (RNAPII) and a reporter for nascent transcripts in unperturbed embryos shows clustering of RNAPII at around 2 min after mitotic exit, followed by progressive dispersal as associated nascent transcripts accumulate later in interphase. Abrupt inhibition of various steps in DNA replication, including origin licensing, origin firing, and polymerization, suppresses post-mitotic RNAPII clustering and transcription in nuclear cycles. We propose that replication dependency defers the onset of transcription so that RNAPII transcribes behind advancing replication forks. The resulting orderly progression can explain how early embryos circumvent transcription-replication conflicts to express essential developmental genes.

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          In brief

          Early zygotic transcription in Drosophila embryos takes place entirely during the short S phases of nuclear division cycles. Cho et al. demonstrate that rapid onset of transcription depends on DNA replication in each cycle. Delayed initiation of transcription allows unimpeded elongation of nascent transcripts behind advancing replication forks.

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          Most cited references38

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.

            State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.
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              Mediator and RNA polymerase II clusters associate in transcription-dependent condensates

              Models of gene control have emerged from genetic and biochemical studies, with limited consideration of the spatial organization and dynamics of key components in living cells. Here we used live cell super-resolution and light sheet imaging to study the organization and dynamics of the Mediator coactivator and RNA polymerase II (Pol II) directly. Mediator and Pol II each form small transient and large stable clusters in living embryonic stem cells. Mediator and Pol II are colocalized in the stable clusters, which associate with chromatin, have properties of phase-separated condensates, and are sensitive to transcriptional inhibitors. We suggest that large clusters of Mediator, recruited by transcription factors at large or clustered enhancer elements, interact with large Pol II clusters in transcriptional condensates in vivo.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                27 October 2022
                18 October 2022
                16 November 2022
                : 41
                : 3
                : 111507
                Affiliations
                [1 ]Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA
                [2 ]Integrative Program for Biological and Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [3 ]Department of Biology, Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
                [4 ]Lead contact
                Author notes
                [* ]Correspondence: ofarrell@ 123456cgl.ucsf.edu

                AUTHOR CONTRIBUTIONS

                Conceptualization, C.-Y.C. and P.H.O.; methodology, C.-Y.C. and J.P.K.; formal analysis, C.-Y.C.; investigation, C.-Y.C.; writing – original draft, C.-Y.C. and P.H.O.; writing – review & editing, J.P.K. and R.J.D.; visualization, C.-Y.C.; validation, J.P.K.; resources, J.P.K. and R.J.D.; supervision, R.J.D. and P.H.O.; funding acquisition, R.J.D. and P.H.O.

                Article
                NIHMS1843693
                10.1016/j.celrep.2022.111507
                9667882
                36261005
                55f8a442-4dfd-46c1-bf08-3b8356cb5b50

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

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                Cell biology
                Cell biology

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