GSE206841
Regulatory functions of cellular energy sensor SnRK1 for nitrate signaling through NLP7 repression
Coordinated metabolism of carbon and nitrogen is essential for optimal plant growth and development. Nitrate is an important molecular signal for plant adaptation to changing environmental conditions, but how nitrate regulates plant growth under carbon deficiency conditions remains unclear. Here, we show that the evolutionarily conserved energy sensor SnRK1 negatively regulates the nitrate signaling pathway. Nitrate promoted plant growth and downstream gene expression, but such effects were significantly repressed when plants were grown under carbon deficiency conditions. Mutation of KIN10, the a-catalytic subunit of SnRK1, partially suppressed the inhibitory effects of carbon deficiency on nitrate-mediated plant growth. KIN10 phosphorylated NLP7, the master regulator of nitrate signaling pathway, to promote its cytoplasmic localization and degradation. Furthermore, nitrate depletion induced KIN10 accumulation, whereas nitrate treatment promoted KIN10 degradation. Such KIN10-mediated NLP7 regulation allows carbon and nitrate availability to control the optimal nitrate signaling and ensures the coordination of carbon and nitrogen metabolism in plants. Overall design: To determine whether KIN10 is involved in nitrate signaling to regulate downstream gene expression,we performed the RNA-sequencing (RNA-Seq) experiments with the roots of wild-type and KIN10 overexpression plants that were grown on MGRL medium containing 5 mM nitrate for 7 days, subjected to nitrogen-free medium for 2 days, and then treated with 5 mM KCl or 5 mM KNO3 for 1 h. To examine the effects of KIN10 on NLP7 functions, we performed the RNA-Seq experiment using the protoplast transient expression system, in which GFP, NLP7-GFP or NLP7-GFP and KIN10-Myc were transformed into the Arabidopsis mesophyll protoplast.Total RNA was extracted with Trizaol RNA extraction kit (Transgene), and the mRNA sequencing libraries construction and sequencing on the BGISEQ-500 platform were performed at Beijing Geonomics institute (BGI). The sequence reads were mapped to the Arabidopsis genome using HISAT and Bowtie2 software, and differential gene expression was analyzed using Noiseq software. Differentially expressed genes were defined by a 2-fold expression difference with a possibility >0.8.