Short-term transcriptomic analysis at organ scale reveals candidate genes involved in low N responses in NUE-contrasting tomato genotypes

Abstract

Background: Understanding the complex regulatory network underlying plant nitrogen (N) responses associated with high Nitrogen Use Efficiency (NUE) is one of the main challenges for sustainable cropping systems. Nitrate (NO₃ ^-), acting as both an N source and a signal molecule, provokes very fast transcriptome reprogramming, allowing plants to adapt to its availability. These changes are genotype- and tissue-specific; thus, the comparison between contrasting genotypes is crucial to uncovering high NUE mechanisms.

Methods: Here, we compared, for the first time, the spatio-temporal transcriptome changes in both root and shoot of two NUE contrasting tomato genotypes, Regina Ostuni (high-NUE) and UC82 (low-NUE), in response to short-term (within 24 h) low (LN) and high (HN) NO₃ ^- resupply.

Results: Using time-series transcriptome data (0, 8, and 24 h), we identified 395 and 482 N-responsive genes differentially expressed (DEGs) between RO and UC82 in shoot and root, respectively. Protein kinase signaling plant hormone signal transduction, and phenylpropanoid biosynthesis were the main enriched metabolic pathways in shoot and root, respectively, and were upregulated in RO compared to UC82. Interestingly, several N transporters belonging to NRT and NPF families, such as NRT2.3, NRT2.4, NPF1.2, and NPF8.3, were found differentially expressed between RO and UC82 genotypes, which might explain the contrasting NUE performances. Transcription factors (TFs) belonging to several families, such as ERF, LOB, GLK, NFYB, ARF, Zinc-finger, and MYB, were differentially expressed between genotypes in response to LN. A complementary Weighted Gene Co-expression Network Analysis (WGCNA) allowed the identification of LN-responsive co-expression modules in RO shoot and root. The regulatory network analysis revealed candidate genes that might have key functions in short-term LN regulation. In particular, an asparagine synthetase (ASNS), a CBL-interacting serine/threonine-protein kinase 1 (CIPK1), a cytokinin riboside 5'-monophosphate phosphoribohydrolase (LOG8), a glycosyltransferase (UGT73C4), and an ERF2 were identified in the shoot, while an LRR receptor-like serine/threonine-protein kinase (FEI1) and two TFs NF-YB5 and LOB37 were identified in the root.

Discussion: Our results revealed potential candidate genes that independently and/or concurrently may regulate short-term low-N response, suggesting a key role played by cytokinin and ROS balancing in early LN regulation mechanisms adopted by the N-use efficient genotype RO.

Keywords: RNAseq; Solanum lycopersicum L.; abiotic stress; nitrogen use efficiency; weighted gene co-expression network analysis (WGCNA).

Figure 1

Tomato genotypes UC82 and Regina Ostuni (RO) grown in hydroponic systems at low N (LN) conditions.

Figure 2

Significant enriched temporal expression profiles of the DEGs identified between genotypes, times, N levels, and their interactions in shoot (A) and root (B). The number on the top refers to the cluster number. The numbers at the bottom are the P-values (left) and the gene number (right) assigned in each cluster, respectively. The lines inside each square represent the trend at the three experimental time points for each cluster.

Figure 3

Expression level of DEGs included in functional classes based on GO term and KEGG pathway enrichment analyses in both tissues. Heatmap of DEGs involved in signal transduction, protein kinases signaling, N-transport, proteolysis, phenylpropanoid, and flavonoid biosynthesis in shoot (A) and root (B), and the differentially expressed TFs in shoot and root (C) in the RO vs. UC82 comparison at 0, 8, and 24 h after low (LN) and high (HN) nitrate resupply.

Figure 4

Merged clusters and dendrograms (A) and module–trait relationships (B) were obtained through the WGCNA analysis using 7,667 and 6,015 DEGs identified in shoot and root, respectively. In the heatmap, each Module Eigengene (ME) was correlated to each experimental condition. Inside each condition (0 h, 8 h-LN, 8 h-HN, 24 h-LN, 24 h-HN), the two genotypes were coded as RO (0) and UC82 (1) (columns 1–5), and ME were also correlated to each experimental condition regardless of the genotype (columns 6–9).

Figure 5

Network visualization of the midnight blue module detected in the shoot (A) and the gray60 module detected in the roots (B). Hub gene annotation (SL3.2) is highlighted on each node.

Figure 6

Expression profiles of the differentially expressed NPF genes in shoot (A) and root (B). Genes were clustered through the DEGreport R package using Variance Stabilized Transformed (VST) data.