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Kinetics of mRNA nuclear export regulate innate immune response gene expression

Author

Listed:
  • Diane Lefaudeux

    (University of California
    University of California)

  • Supriya Sen

    (University of California
    University of California)

  • Kevin Jiang

    (University of California
    University of California)

  • Alexander Hoffmann

    (University of California
    University of California
    University of California)

Abstract

The abundance and stimulus-responsiveness of mature mRNA is thought to be determined by nuclear synthesis, processing, and cytoplasmic decay. However, the rate and efficiency of moving mRNA to the cytoplasm almost certainly contributes, but has rarely been measured. Here, we investigated mRNA export rates for innate immune genes. We generated high spatio-temporal resolution RNA-seq data from endotoxin-stimulated macrophages and parameterized a mathematical model to infer kinetic parameters with confidence intervals. We find that the effective chromatin-to-cytoplasm export rate is gene-specific, varying 100-fold: for some genes, less than 5% of synthesized transcripts arrive in the cytoplasm as mature mRNAs, while others show high export efficiency. Interestingly, effective export rates do not determine temporal gene responsiveness, but complement the wide range of mRNA decay rates; this ensures similar abundances of short- and long-lived mRNAs, which form successive innate immune response expression waves.

Suggested Citation

  • Diane Lefaudeux & Supriya Sen & Kevin Jiang & Alexander Hoffmann, 2022. "Kinetics of mRNA nuclear export regulate innate immune response gene expression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34635-5
    DOI: 10.1038/s41467-022-34635-5
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    References listed on IDEAS

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    1. Ning Wang & Diane Lefaudeux & Anup Mazumder & Jingyi Jessica Li & Alexander Hoffmann, 2021. "Identifying the combinatorial control of signal-dependent transcription factors," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-29, June.
    2. Debashish Ray & Hilal Kazan & Kate B. Cook & Matthew T. Weirauch & Hamed S. Najafabadi & Xiao Li & Serge Gueroussov & Mihai Albu & Hong Zheng & Ally Yang & Hong Na & Manuel Irimia & Leah H. Matzat & R, 2013. "A compendium of RNA-binding motifs for decoding gene regulation," Nature, Nature, vol. 499(7457), pages 172-177, July.
    3. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    5. Valero-Mora, Pedro M., 2010. "ggplot2: Elegant Graphics for Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(b01).
    6. Andreas Raue & Marcel Schilling & Julie Bachmann & Andrew Matteson & Max Schelke & Daniel Kaschek & Sabine Hug & Clemens Kreutz & Brian D Harms & Fabian J Theis & Ursula Klingmüller & Jens Timmer, 2013. "Lessons Learned from Quantitative Dynamical Modeling in Systems Biology," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-17, September.
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