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Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators

Inge De Clercq (UGent) , Jan Van de Velde (UGent) , Xiaopeng Luo (UGent) , Li Liu, Veronique Storme (UGent) , Michiel Van Bel (UGent) , Robin Pottie (UGent) , Dries Vaneechoutte, Frank Van Breusegem (UGent) and Klaas Vandepoele (UGent)
(2021) NATURE PLANTS. 7(4). p.500-513
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Abstract
Gene regulation is a dynamic process in which transcription factors (TFs) play an important role in controlling spatiotemporal gene expression. To enhance our global understanding of regulatory interactions in Arabidopsis thaliana, different regulatory input networks capturing complementary information about DNA motifs, open chromatin, TF-binding and expression-based regulatory interactions were combined using a supervised learning approach, resulting in an integrated gene regulatory network (iGRN) covering 1,491 TFs and 31,393 target genes (1.7 million interactions). This iGRN outperforms the different input networks to predict known regulatory interactions and has a similar performance to recover functional interactions compared to state-of-the-art experimental methods. The iGRN correctly inferred known functions for 681 TFs and predicted new gene functions for hundreds of unknown TFs. For regulators predicted to be involved in reactive oxygen species (ROS) stress regulation, we confirmed in total 75% of TFs with a function in ROS and/or physiological stress responses. This includes 13 ROS regulators, previously not connected to any ROS or stress function, that were experimentally validated in our ROS-specific phenotypic assays of loss- or gain-of-function lines. In conclusion, the presented iGRN offers a high-quality starting point to enhance our understanding of gene regulation in plants by integrating different experimental data types.

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MLA
De Clercq, Inge, et al. “Integrative Inference of Transcriptional Networks in Arabidopsis Yields Novel ROS Signalling Regulators.” NATURE PLANTS, vol. 7, no. 4, 2021, pp. 500–13, doi:10.1038/s41477-021-00894-1.
APA
De Clercq, I., Van de Velde, J., Luo, X., Liu, L., Storme, V., Van Bel, M., … Vandepoele, K. (2021). Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators. NATURE PLANTS, 7(4), 500–513. https://doi.org/10.1038/s41477-021-00894-1
Chicago author-date
De Clercq, Inge, Jan Van de Velde, Xiaopeng Luo, Li Liu, Veronique Storme, Michiel Van Bel, Robin Pottie, Dries Vaneechoutte, Frank Van Breusegem, and Klaas Vandepoele. 2021. “Integrative Inference of Transcriptional Networks in Arabidopsis Yields Novel ROS Signalling Regulators.” NATURE PLANTS 7 (4): 500–513. https://doi.org/10.1038/s41477-021-00894-1.
Chicago author-date (all authors)
De Clercq, Inge, Jan Van de Velde, Xiaopeng Luo, Li Liu, Veronique Storme, Michiel Van Bel, Robin Pottie, Dries Vaneechoutte, Frank Van Breusegem, and Klaas Vandepoele. 2021. “Integrative Inference of Transcriptional Networks in Arabidopsis Yields Novel ROS Signalling Regulators.” NATURE PLANTS 7 (4): 500–513. doi:10.1038/s41477-021-00894-1.
Vancouver
1.
De Clercq I, Van de Velde J, Luo X, Liu L, Storme V, Van Bel M, et al. Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators. NATURE PLANTS. 2021;7(4):500–13.
IEEE
[1]
I. De Clercq et al., “Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators,” NATURE PLANTS, vol. 7, no. 4, pp. 500–513, 2021.
@article{8704989,
  abstract     = {{Gene regulation is a dynamic process in which transcription factors (TFs) play an important role in controlling spatiotemporal gene expression. To enhance our global understanding of regulatory interactions in Arabidopsis thaliana, different regulatory input networks capturing complementary information about DNA motifs, open chromatin, TF-binding and expression-based regulatory interactions were combined using a supervised learning approach, resulting in an integrated gene regulatory network (iGRN) covering 1,491 TFs and 31,393 target genes (1.7 million interactions). This iGRN outperforms the different input networks to predict known regulatory interactions and has a similar performance to recover functional interactions compared to state-of-the-art experimental methods. The iGRN correctly inferred known functions for 681 TFs and predicted new gene functions for hundreds of unknown TFs. For regulators predicted to be involved in reactive oxygen species (ROS) stress regulation, we confirmed in total 75% of TFs with a function in ROS and/or physiological stress responses. This includes 13 ROS regulators, previously not connected to any ROS or stress function, that were experimentally validated in our ROS-specific phenotypic assays of loss- or gain-of-function lines. In conclusion, the presented iGRN offers a high-quality starting point to enhance our understanding of gene regulation in plants by integrating different experimental data types.}},
  author       = {{De Clercq, Inge and Van de Velde, Jan and Luo, Xiaopeng and Liu, Li and Storme, Veronique and Van Bel, Michiel and Pottie, Robin and Vaneechoutte, Dries and Van Breusegem, Frank and Vandepoele, Klaas}},
  issn         = {{2055-026X}},
  journal      = {{NATURE PLANTS}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{500--513}},
  title        = {{Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators}},
  url          = {{http://doi.org/10.1038/s41477-021-00894-1}},
  volume       = {{7}},
  year         = {{2021}},
}
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