Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 25; pp. 6494 - 6499
• Main Authors: Varala, Kranthi, Marshall-Colón, Amy, Cirrone, Jacopo, Brooks, Matthew D., Pasquino, Angelo V., Léran, Sophie, Mittal, Shipra, Rock, Tara M., Edwards, Molly B., Kim, Grace J., Ruffel, Sandrine, McCombie, W. Richard, Shasha, Dennis, Coruzzi, Gloria M.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 19.06.2018
• Series: From the Cover
• Subjects: Arabidopsis - genetics; Arabidopsis - metabolism; Arabidopsis Proteins - genetics; Biological Sciences; Flowers & plants; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Plant - genetics; Gene Regulatory Networks - genetics; Genes; Genetics; Genomes; Learning algorithms; Life Sciences; Logic; Machine learning; Nitrogen; Nitrogen - metabolism; Protein Binding - genetics; Pruning; Regulators; Root development; Signal Transduction - genetics; Signaling; Transcription Factors - genetics; Transcription, Genetic - genetics; systems biology; plant biology; network inference; transcriptional dynamics; nitrogen assimilation; biologie des systèmes; transcription factors; assimilation azotée; biologie de la plante; réseau de régulation de géne; facteur de transcription
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1721487115

This study exploits time, the relatively unexplored fourth dimension of gene regulatory networks (GRNs), to learn the temporal transcriptional logic underlying dynamic nitrogen (N) signaling in plants. Our “just-in-time” analysis of time-series transcriptome data uncovered a temporal cascade of cis elements underlying dynamic N signaling. To infer transcription factor (TF)-target edges in a GRN, we applied a time-based machine learning method to 2,174 dynamic N-responsive genes. We experimentally determined a network precision cutoff, using TF-regulated genome-wide targets of three TF hubs (CRF4, SNZ, and CDF1), used to “prune” the network to 155 TFs and 608 targets. This network precision was reconfirmed using genome-wide TF-target regulation data for four additional TFs (TGA1, HHO5/6, and PHL1) not used in network pruning. These higher-confidence edges in the GRN were further filtered by independent TF-target binding data, used to calculate a TF “N-specificity” index. This refined GRN identifies the temporal relationship of known/validated regulators of N signaling (NLP7/8, TGA1/4, NAC4, HRS1, and LBD37/38/39) and 146 additional regulators. Six TFs—CRF4, SNZ, CDF1, HHO5/6, and PHL1—validated herein regulate a significant number of genes in the dynamic N response, targeting 54% of N-uptake/assimilation pathway genes. Phenotypically, inducible overexpression of CRF4 in planta regulates genes resulting in altered biomass, root development, and 15NO₃⁻ uptake, specifically under low-N conditions. This dynamic N-signaling GRN now provides the temporal “transcriptional logic” for 155 candidate TFs to improve nitrogen use efficiency with potential agricultural applications. Broadly, these time-based approaches can uncover the temporal transcriptional logic for any biological response system in biology, agriculture, or medicine.