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Widening the landscape of transcriptional regulation of green algal photoprotection

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  • Marius Arend

    (University of Potsdam
    Max-Planck-Institute of Molecular Plant Physiology
    Center of Plant Systems Biology and Biotechnology)

  • Yizhong Yuan

    (University of Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV)

  • M. Águila Ruiz-Sola

    (University of Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV
    Universidad de Sevilla-CSIC)

  • Nooshin Omranian

    (University of Potsdam
    Max-Planck-Institute of Molecular Plant Physiology
    Center of Plant Systems Biology and Biotechnology)

  • Zoran Nikoloski

    (University of Potsdam
    Max-Planck-Institute of Molecular Plant Physiology
    Center of Plant Systems Biology and Biotechnology)

  • Dimitris Petroutsos

    (University of Grenoble Alpes, CNRS, CEA, INRAE, IRIG-LPCV)

Abstract

Availability of light and CO2, substrates of microalgae photosynthesis, is frequently far from optimal. Microalgae activate photoprotection under strong light, to prevent oxidative damage, and the CO2 Concentrating Mechanism (CCM) under low CO2, to raise intracellular CO2 levels. The two processes are interconnected; yet, the underlying transcriptional regulators remain largely unknown. Employing a large transcriptomic data compendium of Chlamydomonas reinhardtii’s responses to different light and carbon supply, we reconstruct a consensus genome-scale gene regulatory network from complementary inference approaches and use it to elucidate transcriptional regulators of photoprotection. We show that the CCM regulator LCR1 also controls photoprotection, and that QER7, a Squamosa Binding Protein, suppresses photoprotection- and CCM-gene expression under the control of the blue light photoreceptor Phototropin. By demonstrating the existence of regulatory hubs that channel light- and CO2-mediated signals into a common response, our study provides an accessible resource to dissect gene expression regulation in this microalga.

Suggested Citation

  • Marius Arend & Yizhong Yuan & M. Águila Ruiz-Sola & Nooshin Omranian & Zoran Nikoloski & Dimitris Petroutsos, 2023. "Widening the landscape of transcriptional regulation of green algal photoprotection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38183-4
    DOI: 10.1038/s41467-023-38183-4
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    References listed on IDEAS

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    1. Dimitris Petroutsos & Ryutaro Tokutsu & Shinichiro Maruyama & Serena Flori & Andre Greiner & Leonardo Magneschi & Loic Cusant & Tilman Kottke & Maria Mittag & Peter Hegemann & Giovanni Finazzi & Jun M, 2016. "A blue-light photoreceptor mediates the feedback regulation of photosynthesis," Nature, Nature, vol. 537(7621), pages 563-566, September.
    2. Vân Anh Huynh-Thu & Alexandre Irrthum & Louis Wehenkel & Pierre Geurts, 2010. "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-10, September.
    3. Xiao-Ping Li & Olle Björkman & Connie Shih & Arthur R. Grossman & Magnus Rosenquist & Stefan Jansson & Krishna K. Niyogi, 2000. "A pigment-binding protein essential for regulation of photosynthetic light harvesting," Nature, Nature, vol. 403(6768), pages 391-395, January.
    4. Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
    5. Asami Hiyama & Atsushi Takemiya & Shintaro Munemasa & Eiji Okuma & Naoyuki Sugiyama & Yasuomi Tada & Yoshiyuki Murata & Ken-ichiro Shimazaki, 2017. "Blue light and CO2 signals converge to regulate light-induced stomatal opening," Nature Communications, Nature, vol. 8(1), pages 1-13, December.
    6. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    7. Graham Peers & Thuy B. Truong & Elisabeth Ostendorf & Andreas Busch & Dafna Elrad & Arthur R. Grossman & Michael Hippler & Krishna K. Niyogi, 2009. "An ancient light-harvesting protein is critical for the regulation of algal photosynthesis," Nature, Nature, vol. 462(7272), pages 518-521, November.
    8. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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