Transcriptomic and network analyses reveal distinct nitrate responses in light and dark in rice leaves (Oryza sativa Indica var. Panvel1)

Transcriptional Modulation during Photomorphogenesis in Rice Seedlings

Light is one of the most important factors regulating plant gene expression patterns, metabolism, physiology, growth, and development. To explore how light may induce or alter transcript splicing, we conducted RNA-Seq-based transcriptome analyses by comparing the samples harvested as etiolated seedlings grown under continuous dark conditions vs. the light-treated green seedlings. The study aims to reveal differentially regulated protein-coding genes and novel long noncoding RNAs (lncRNAs), their light-induced alternative splicing, and their association with biological pathways. We identified 14,766 differentially expressed genes, of which 4369 genes showed alternative splicing. We observed that genes mapped to the plastid-localized methyl-erythritol-phosphate (MEP) pathway were light-upregulated compared to the cytosolic mevalonate (MVA) pathway genes. Many of these genes also undergo splicing. These pathways provide crucial metabolite precursors for the biosynthesis of secondary metabolic compounds needed for chloroplast biogenesis, the establishment of a successful photosynthetic apparatus, and photomorphogenesis. In the chromosome-wide survey of the light-induced transcriptome, we observed intron retention as the most predominant splicing event. In addition, we identified 1709 novel lncRNA transcripts in our transcriptome data. This study provides insights on light-regulated gene expression and alternative splicing in rice.

Nitrate-responsive transcriptome analysis of rice RGA1 mutant reveals the role of G-protein alpha subunit in negative regulation of nitrogen-sensitivity and use efficiency

KEY MESSAGE

Nitrate-responsive transcriptomic, phenotypic and physiological analyses of rice RGA1 mutant revealed many novel RGA1-regulated genes/processes/traits related to nitrogen use efficiency, and provided robust genetic evidence of RGA1-regulation of NUE. Nitrogen (N) use efficiency (NUE) is important for sustainable agriculture. G-protein signalling was implicated in N-response/NUE in rice, but needed firm genetic characterization of the role of alpha subunit (RGA1). The knock-out mutant of RGA1 in japonica rice exhibited lesser nitrate-dose sensitivity than the wild type (WT), in yield and NUE. We, therefore, investigated its genomewide nitrate-response relative to WT. It revealed 3416 differentially expressed genes (DEGs), including 719 associated with development, grain yield and phenotypic traits for NUE. The upregulated DEGs were related to photosynthesis, chlorophyll, tetrapyrrole and porphyrin biosynthesis, while the downregulated DEGs belonged to cellular protein metabolism and transport, small GTPase signalling, cell redox homeostasis, etc. We validated 26 nitrate-responsive DEGs across functional categories by RT-qPCR. Physiological validation of nitrate-response in the mutant and the WT at 1.5 and 15 mM doses revealed higher chlorophyll and stomatal length but decreased stomatal density, conductance and transpiration. The consequent increase in photosynthesis and water use efficiency may have contributed to better yield and NUE in the mutant, whereas the WT was N-dose sensitive. The mutant was not as N-dose-responsive as the WT in shoot/root growth, productive tillers and heading date, but equally responsive as WT in total N and protein content. The RGA1 mutant was less impacted by higher N-dose or salt stress in terms of yield, protein content, photosynthetic performance, relative water content, water use efficiency and catalase activity. PPI network analyses revealed known NUE-related proteins as RGA1 interactors. Therefore, RGA1 negatively regulates N-dose sensitivity and NUE in rice.

Weighted gene co-expression network analysis of nitrogen (N)-responsive genes and the putative role of G-quadruplexes in N use efficiency (NUE) in rice

Rice is an important target to improve crop nitrogen (N) use efficiency (NUE), and the identification and shortlisting of the candidate genes are still in progress. We analyzed data from 16 published N-responsive transcriptomes/microarrays to identify, eight datasets that contained the maximum number of 3020 common genes, referred to as N-responsive genes. These include different classes of transcription factors, transporters, miRNA targets, kinases and events of post-translational modifications. A Weighted gene co-expression network analysis (WGCNA) with all the 3020 N-responsive genes revealed 15 co-expression modules and their annotated biological roles. Protein-protein interaction network analysis of the main module revealed the hub genes and their functional annotation revealed their involvement in the ubiquitin process. Further, the occurrences of G-quadruplex sequences were examined, which are known to play important roles in epigenetic regulation but are hitherto unknown in N-response/NUE. Out of the 3020 N-responsive genes studied, 2298 contained G-quadruplex sequences. We compared these N-responsive genes containing G-quadruplex sequences with the 3601 genes we previously identified as NUE-related (for being both N-responsive and yield-associated). This analysis revealed 389 (17%) NUE-related genes containing G-quadruplex sequences. These genes may be involved in the epigenetic regulation of NUE, while the rest of the 83% (1811) genes may regulate NUE through genetic mechanisms and/or other epigenetic means besides G-quadruplexes. A few potentially important genes/processes identified as associated with NUE were experimentally validated in a pair of rice genotypes contrasting for NUE. The results from the WGCNA and G4 sequence analysis of N-responsive genes helped identify and shortlist six genes as candidates to improve NUE. Further, the hitherto unavailable segregation of genetic and epigenetic gene targets could aid in informed interventions through genetic and epigenetic means of crop improvement.

Genome-Wide Urea Response in Rice Genotypes Contrasting for Nitrogen Use Efficiency

Rice is an ideal crop for improvement of nitrogen use efficiency (NUE), especially with urea, its predominant fertilizer. There is a paucity of studies on rice genotypes contrasting for NUE. We compared low urea-responsive transcriptomes of contrasting rice genotypes, namely Nidhi (low NUE) and Panvel1 (high NUE). Transcriptomes of whole plants grown with media containing normal (15 mM) and low urea (1.5 mM) revealed 1497 and 2819 differentially expressed genes (DEGs) in Nidhi and Panvel1, respectively, of which 271 were common. Though 1226 DEGs were genotype-specific in Nidhi and 2548 in Panvel1, there was far higher commonality in underlying processes. High NUE is associated with the urea-responsive regulation of other nutrient transporters, miRNAs, transcription factors (TFs) and better photosynthesis, water use efficiency and post-translational modifications. Many of their genes co-localized to NUE-QTLs on chromosomes 1, 3 and 9. A field evaluation under different doses of urea revealed better agronomic performance including grain yield, transport/uptake efficiencies and NUE of Panvel1. Comparison of our urea-based transcriptomes with our previous nitrate-based transcriptomes revealed many common processes despite large differences in their expression profiles. Our model proposes that differential involvement of transporters and TFs, among others, contributes to better urea uptake, translocation, utilization, flower development and yield for high NUE.

Comparative Transcriptomic Analyses of Nitrate-Response in Rice Genotypes With Contrasting Nitrogen Use Efficiency Reveals Common and Genotype-Specific Processes, Molecular Targets and Nitrogen Use Efficiency-Candidates

The genetic basis for nitrogen (N)-response and N use efficiency (NUE) must be found in N-responsive gene expression or protein regulation. Our transcriptomic analysis of nitrate response in two contrasting rice genotypes of Oryza sativa ssp. Indica (Nidhi with low NUE and Panvel1 with high NUE) revealed the processes/functions underlying differential N-response/NUE. The microarray analysis of low nitrate response (1.5 mM) relative to normal nitrate control (15 mM) used potted 21-days old whole plants. It revealed 1,327 differentially expressed genes (DEGs) exclusive to Nidhi and 666 exclusive to Panvel1, apart from 70 common DEGs, of which 10 were either oppositely expressed or regulated to different extents. Gene ontology analyses revealed that photosynthetic processes were among the very few processes common to both the genotypes in low N response. Those unique to Nidhi include cell division, nitrogen utilization, cytoskeleton, etc. in low N-response, whereas those unique to Panvel1 include signal transduction, protein import into the nucleus, and mitochondria. This trend of a few common but mostly unique categories was also true for transporters, transcription factors, microRNAs, and post-translational modifications, indicating their differential involvement in Nidhi and Panvel1. Protein-protein interaction networks constructed using DEG-associated experimentally validated interactors revealed subnetworks involved in cytoskeleton organization, cell wall, etc. in Nidhi, whereas in Panvel1, it was chloroplast development. NUE genes were identified by selecting yield-related genes from N-responsive DEGs and their co-localization on NUE-QTLs revealed the differential distribution of NUE-genes between genotypes but on the same chromosomes 1 and 3. Such hotspots are important for NUE breeders.

The Interaction Between Nitrogen Supply and Light Quality Modulates Plant Growth and Resource Allocation

Nitrogen availability and light quality affect plant resource allocation, but their interaction is poorly understood. Herein, we analyzed the growth and allocation of dry matter and nitrogen using lettuce (Lactuca sativa L.) as a plant model in a factorial experiment combining three light regimes (100% red light, R; 50% red light + 50% blue light, RB; 100% blue light, B) and two nitrogen rates (low, 0.1 mM N; high, 10 mM N). Red light increased shoot dry weight in relation to both B and RB irrespective of nitrogen supply. Blue light favored root growth under low nitrogen. Allometric analysis showed lower allocation to leaf in response to blue light under low nitrogen and similar leaf allocation under high nitrogen. A difference in allometric slopes between low nitrogen and high nitrogen in treatments with blue light reflected a strong interaction effect on root-to-shoot biomass allocation. Shoot nitrate concentration increased with light exposure up to 14 h in both nitrogen treatments, was higher under blue light with high nitrogen, and varied little with light quality under low nitrogen. Shoot nitrogen concentration, nitrogen nutrition index, and shoot NR activity increased in response to blue light. We conclude that the interaction between blue light and nitrogen supply modulates dry mass and nitrogen allocation between the shoot and root.

What signals the glyoxalase pathway in plants?

Glyoxalase (GLY) system, comprising of GLYI and GLYII enzymes, has emerged as one of the primary methylglyoxal (MG) detoxification pathways with an indispensable role during abiotic and biotic stresses. MG homeostasis is indeed very closely guarded by the cell as its higher levels are cytotoxic for the organism. The dynamic responsiveness of MG-metabolizing GLY pathway to both endogenous cues such as, phytohormones, nutrient status, etc., as well as external environmental fluctuations (abiotic and biotic stresses) indicates that a tight regulation occurs in the cell to maintain physiological levels of MG in the system. Interestingly, GLY pathway is also manipulated by its substrates and reaction products. Hence, an investigation of signalling and regulatory aspects of GLY pathway would be worthwhile. Herein, we have attempted to converge all known factors acting as signals or directly regulating GLYI/II enzymes in plants. Further, we also discuss how crosstalk between these different signal molecules might facilitate the regulation of glyoxalase pathway. We believe that MG detoxification is controlled by intricate mechanisms involving a plethora of signal molecules.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Background

Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.

Methods

We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.

Results

The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Meta-Analysis of Yield-Related and N-Responsive Genes Reveals Chromosomal Hotspots, Key Processes and Candidate Genes for Nitrogen-Use Efficiency in Rice

Nitrogen-use efficiency (NUE) is a function of N-response and yield that is controlled by many genes and phenotypic parameters that are poorly characterized. This study compiled all known yield-related genes in rice and mined them from the N-responsive microarray data to find 1,064 NUE-related genes. Many of them are novel genes hitherto unreported as related to NUE, including 80 transporters, 235 transcription factors (TFs), 44 MicroRNAs (miRNAs), 91 kinases, and 8 phosphatases. They were further shortlisted to 62 NUE-candidate genes following hierarchical methods, including quantitative trait locus (QTL) co-localization, functional evaluation in the literature, and protein-protein interactions (PPIs). They were localized to chromosomes 1, 3, 5, and 9, of which chromosome 1 with 26 genes emerged as a hotspot for NUE spanning 81% of the chromosomes. Further, co-localization of the NUE genes on NUE-QTLs resolved differences in the earlier studies that relied mainly on N-responsive genes regardless of their role in yield. Functional annotations and PPIs for all the 1,064 NUE-related genes and also the shortlisted 62 candidates revealed transcription, redox, phosphorylation, transport, development, metabolism, photosynthesis, water deprivation, and hormonal and stomatal function among the prominent processes. In silico expression analysis confirmed differential expression of the 62 NUE-candidate genes in a tissue/stage-specific manner. Experimental validation in two contrasting genotypes revealed that high NUE rice shows better photosynthetic performance, transpiration efficiency and internal water-use efficiency in comparison to low NUE rice. Feature Selection Analysis independently identified one-third of the common genes at every stage of hierarchical shortlisting, offering 6 priority targets to validate for improving the crop NUE.

Nitrogen Use Efficiency Phenotype and Associated Genes: Roles of Germination, Flowering, Root/Shoot Length and Biomass

Crop improvement for Nitrogen Use Efficiency (NUE) requires a well-defined phenotype and genotype, especially for different N-forms. As N-supply enhances growth, we comprehensively evaluated 25 commonly measured phenotypic parameters for N response using 4 N treatments in six indica rice genotypes. For this, 32 replicate potted plants were grown in the green-house on nutrient-depleted sand. They were fertilized to saturation with media containing either nitrate or urea as the sole N source at normal (15 mM N) or low level (1.5 mM N). The variation in N-response among genotypes differed by N form/dose and increased developmentally from vegetative to reproductive parameters. This indicates survival adaptation by reinforcing variation in every generation. Principal component analysis segregated vegetative parameters from reproduction and germination. Analysis of variance revealed that relative to low level, normal N facilitated germination, flowering and vegetative growth but limited yield and NUE. Network analysis for the most connected parameters, their correlation with yield and NUE, ranking by Feature selection and validation by Partial least square discriminant analysis enabled shortlisting of eight parameters for NUE phenotype. It constitutes germination and flowering, shoot/root length and biomass parameters, six of which were common to nitrate and urea. Field-validation confirmed the NUE differences between two genotypes chosen phenotypically. The correspondence between multiple approaches in shortlisting parameters for NUE makes it a novel and robust phenotyping methodology of relevance to other plants, nutrients or other complex traits. Thirty-Four N-responsive genes associated with the phenotype have also been identified for genotypic characterization of NUE.

Novel Aspects of Nitrate Regulation in Arabidopsis

Nitrogen (N) is one of the most essential macronutrients for plant growth and development. Nitrate (NO₃ ^-), the major form of N that plants uptake from the soil, acts as an important signaling molecule in addition to its nutritional function. Over the past decade, significant progress has been made in identifying new components involved in NO₃ ^- regulation and starting to unravel the NO₃ ^- regulatory network. Great reviews have been made recently by scientists on the key regulators in NO₃ ^- signaling, NO₃ ^- effects on plant development, and its crosstalk with phosphorus (P), potassium (K), hormones, and calcium signaling. However, several novel aspects of NO₃ ^- regulation have not been previously reviewed in detail. Here, we mainly focused on the recent advances of post-transcriptional regulation and non-coding RNA (ncRNAs) in NO₃ ^- signaling, and NO₃ ^- regulation on leaf senescence and the circadian clock. It will help us to extend the general picture of NO₃ ^- regulation and provide a basis for further exploration of NO₃ ^- regulatory network.