Abstract
Background: Nitrogen (N) is a key macronutrient essential for plant growth, and its availability has a strong influence on crop development. The application of synthetic N fertilizers on crops has increased substantially in recent decades; however, the applied N is not fully utilized due to the low N use efficiency of crops. To overcome this limitation, it is important to understand the genome-wide responses and functions of key genes and potential regulatory factors in N metabolism. Results: Here, we characterized changes in the rice (Oryza sativa) transcriptome, including genes, newly identified putative long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) and their target mRNAs in response to N starvation using four different transcriptome approaches. Analysis of rice genes involved in N metabolism and/or transport using strand-specific RNA-Seq identified 2588 novel putative lncRNA encoding loci. Analysis of previously published RNA-Seq datasets revealed a group of N starvation-responsive lncRNAs showing differential expression under other abiotic stress conditions. Poly A-primed sequencing (2P-Seq) revealed alternatively polyadenylated isoforms of N starvation-responsive lncRNAs and provided precise 3' end information on the transcript models of these lncRNAs. Analysis of small RNA-Seq data identified N starvation-responsive miRNAs and down-regulation of miR169 family members, causing de-repression of NF-YA, as confirmed by strand-specific RNA-Seq and qRT-PCR. Moreover, we profiled the N starvation-responsive down-regulation of root-specific miRNA, osa-miR444a.4-3p, and Degradome sequencing confirmed MADS25 as a novel target gene. Conclusions: In this study, we used a combination of multiple RNA-Seq analyses to extensively profile the expression of genes, newly identified lncRNAs, and microRNAs in N-starved rice roots and shoots. Data generated in this study provide an in-depth understanding of the regulatory pathways modulated by N starvation-responsive miRNAs. The results of comprehensive, large-scale data analysis provide valuable information on multiple aspects of the rice transcriptome, which may be useful in understanding the responses of rice plants to changes in the N supply status of soil.