Cross-Species Network Analysis Uncovers Conserved Nitrogen-Regulated Network Modules in Rice

Nitrogen sensing and regulatory networks: it's about time and space

A plant's response to external and internal nitrogen signals/status relies on sensing and signaling mechanisms that operate across spatial and temporal dimensions. From a comprehensive systems biology perspective, this involves integrating nitrogen responses in different cell types and over long distances to ensure organ coordination in real time and yield practical applications. In this prospective review, we focus on novel aspects of nitrogen (N) sensing/signaling uncovered using temporal and spatial systems biology approaches, largely in the model Arabidopsis. The temporal aspects span: transcriptional responses to N-dose mediated by Michaelis-Menten kinetics, the role of the master NLP7 transcription factor as a nitrate sensor, its nitrate-dependent TF nuclear retention, its "hit-and-run" mode of target gene regulation, and temporal transcriptional cascade identified by "network walking." Spatial aspects of N-sensing/signaling have been uncovered in cell type-specific studies in roots and in root-to-shoot communication. We explore new approaches using single-cell sequencing data, trajectory inference, and pseudotime analysis as well as machine learning and artificial intelligence approaches. Finally, unveiling the mechanisms underlying the spatial dynamics of nitrogen sensing/signaling networks across species from model to crop could pave the way for translational studies to improve nitrogen-use efficiency in crops. Such outcomes could potentially reduce the detrimental effects of excessive fertilizer usage on groundwater pollution and greenhouse gas emissions.

A Survey of the Transcriptomic Resources in Durum Wheat: Stress Responses, Data Integration and Exploitation

Durum wheat (Triticum turgidum subsp. durum (Desf.) Husn.) is an allotetraploid cereal crop of worldwide importance, given its use for making pasta, couscous, and bulgur. Under climate change scenarios, abiotic (e.g., high and low temperatures, salinity, drought) and biotic (mainly exemplified by fungal pathogens) stresses represent a significant limit for durum cultivation because they can severely affect yield and grain quality. The advent of next-generation sequencing technologies has brought a huge development in transcriptomic resources with many relevant datasets now available for durum wheat, at various anatomical levels, also focusing on phenological phases and environmental conditions. In this review, we cover all the transcriptomic resources generated on durum wheat to date and focus on the corresponding scientific insights gained into abiotic and biotic stress responses. We describe relevant databases, tools and approaches, including connections with other "omics" that could assist data integration for candidate gene discovery for bio-agronomical traits. The biological knowledge summarized here will ultimately help in accelerating durum wheat breeding.

Transcriptome and Co-Expression Network Analysis Reveals the Molecular Mechanism of Rice Root Systems in Response to Low-Nitrogen Conditions

Nitrogen is an important nutrient for plant growth and essential metabolic processes. Roots integrally obtain nutrients from soil and are closely related to the growth and development of plants. In this study, the morphological analysis of rice root tissues collected at different time points under low-nitrogen and normal nitrogen conditions demonstrated that, compared with normal nitrogen treatment, the root growth and nitrogen use efficiency (NUE) of rice under low-nitrogen treatment were significantly improved. To better understand the molecular mechanisms of the rice root system’s response to low-nitrogen conditions, a comprehensive transcriptome analysis of rice seedling roots under low-nitrogen and control conditions was conducted in this study. As a result, 3171 differentially expressed genes (DEGs) were identified. Rice seedling roots enhance NUE and promote root development by regulating the genes related to nitrogen absorption and utilization, carbon metabolism, root growth and development, and phytohormones, thereby adapting to low-nitrogen conditions. A total of 25,377 genes were divided into 14 modules using weighted gene co-expression network analysis (WGCNA). Two modules were significantly associated with nitrogen absorption and utilization. A total of 8 core genes and 43 co-expression candidates related to nitrogen absorption and utilization were obtained in these two modules. Further studies on these genes will contribute to the understanding of low-nitrogen adaptation and nitrogen utilization mechanisms in rice.

Interspecific comparison of gene expression profiles using machine learning

Interspecific gene comparisons are the keystones for many areas of biological research and are especially important for the translation of knowledge from model organisms to economically important species. Currently they are hampered by the low resolution of methods based on sequence analysis and by the complex evolutionary history of eukaryotic genes. This is especially critical for plants, whose genomes are shaped by multiple whole genome duplications and subsequent gene loss. This requires the development of new methods for comparing the functions of genes in different species. Here, we report ISEEML (Interspecific Similarity of Expression Evaluated using Machine Learning)-a novel machine learning-based algorithm for interspecific gene classification. In contrast to previous studies focused on sequence similarity, our algorithm focuses on functional similarity inferred from the comparison of gene expression profiles. We propose novel metrics for expression pattern similarity-expression score (ES)-that is suitable for species with differing morphologies. As a proof of concept, we compare detailed transcriptome maps of Arabidopsis thaliana, the model species, Zea mays (maize) and Fagopyrum esculentum (common buckwheat), which are species that represent distant clades within flowering plants. The classifier resulted in an AUC of 0.91; under the ES threshold of 0.5, the specificity was 94%, and sensitivity was 72%.

Identification of growth regulators using cross-species network analysis in plants

With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.

Genome-Scale Investigation of GARP Family Genes Reveals Their Pivotal Roles in Nutrient Stress Resistance in Allotetraploid Rapeseed

The GARP genes are plant-specific transcription factors (TFs) and play key roles in regulating plant development and abiotic stress resistance. However, few systematic analyses of GARPs have been reported in allotetraploid rapeseed (Brassica napus L.) yet. In the present study, a total of 146 BnaGARP members were identified from the rapeseed genome based on the sequence signature. The BnaGARP TFs were divided into five subfamilies: ARR, GLK, NIGT1/HRS1/HHO, KAN, and PHL subfamilies, and the members within the same subfamilies shared similar exon-intron structures and conserved motif configuration. Analyses of the Ka/Ks ratios indicated that the GARP family principally underwent purifying selection. Several cis-acting regulatory elements, essential for plant growth and diverse biotic and abiotic stresses, were identified in the promoter regions of BnaGARPs. Further, 29 putative miRNAs were identified to be targeting BnaGARPs. Differential expression of BnaGARPs under low nitrate, ammonium toxicity, limited phosphate, deficient boron, salt stress, and cadmium toxicity conditions indicated their potential involvement in diverse nutrient stress responses. Notably, BnaA9.HHO1 and BnaA1.HHO5 were simultaneously transcriptionally responsive to these nutrient stresses in both hoots and roots, which indicated that BnaA9.HHO1 and BnaA1.HHO5 might play a core role in regulating rapeseed resistance to nutrient stresses. Therefore, this study would enrich our understanding of molecular characteristics of the rapeseed GARPs and will provide valuable candidate genes for further in-depth study of the GARP-mediated nutrient stress resistance in rapeseed.

Gene regulatory networks shape developmental plasticity of root cell types under water extremes in rice

Understanding how roots modulate development under varied irrigation or rainfall is crucial for development of climate-resilient crops. We established a toolbox of tagged rice lines to profile translating mRNAs and chromatin accessibility within specific cell populations. We used these to study roots in a range of environments: plates in the lab, controlled greenhouse stress and recovery conditions, and outdoors in a paddy. Integration of chromatin and mRNA data resolves regulatory networks of the following: cycle genes in proliferating cells that attenuate DNA synthesis under submergence; genes involved in auxin signaling, the circadian clock, and small RNA regulation in ground tissue; and suberin biosynthesis, iron transporters, and nitrogen assimilation in endodermal/exodermal cells modulated with water availability. By applying a systems approach, we identify known and candidate driver transcription factors of water-deficit responses and xylem development plasticity. Collectively, this resource will facilitate genetic improvements in root systems for optimal climate resilience.

Global gene expression profiling under nitrogen stress identifies key genes involved in nitrogen stress adaptation in maize (Zea mays L.)

Maize is a heavy consumer of fertilizer nitrogen (N) which not only results in the high cost of cultivation but may also lead to environmental pollution. Therefore, there is a need to develop N-use efficient genotypes, a prerequisite for which is a greater understanding of N-deficiency stress adaptation. In this study, comparative transcriptome analysis was performed using leaf and root tissues from contrasting inbred lines, viz., DMI 56 (tolerant to N stress) and DMI 81 (susceptible to N stress) to delineate the differentially expressed genes (DEGs) under low-N stress. The contrasting lines were grown hydroponically in modified Hoagland solution having either sufficient- or deficient-N, followed by high-throughput RNA-sequencing. A total of 8 sequencing libraries were prepared and 88–97% of the sequenced raw reads were mapped to the reference B73 maize genome. Genes with a p value ≤ 0.05 and fold change of ≥ 2.0 or ≤ − 2 were considered as DEGs in various combinations performed between susceptible and tolerant genotypes. DEGs were further classified into different functional categories and pathways according to their putative functions. Gene Ontology based annotation of these DEGs identified three different functional categories: biological processes, molecular function, and cellular component. The KEGG and Mapman based analysis revealed that most of the DEGs fall into various metabolic pathways, biosynthesis of secondary metabolites, signal transduction, amino acid metabolism, N-assimilation and metabolism, and starch metabolism. Some of the key genes involved in N uptake (high-affinity nitrate transporter 2.2 and 2.5), N assimilation and metabolism (glutamine synthetase, asparagine synthetase), redox homeostasis (SOD, POX), and transcription factors (MYB36, AP2-EREBP) were found to be highly expressed in the tolerant genotype compared to susceptible one. The candidate genes identified in the present study might be playing a pivotal role in low-N stress adaptation in maize and hence could be useful in augmenting further research on N metabolism and development of N-deficiency tolerant maize cultivars.

Gene regulatory circuitry of plant-environment interactions: scaling from cells to the field

Plant growth and development is the product of layers of sensing and regulation that are modulated by multifactorial environmental cues. Innovations in genomics currently allow gene regulatory control to be quantified at multiple scales and high resolution in defined cell populations and even in individual cells or nuclei in plants. The application of these 'omic technologies in highly controlled, as well as field environments is revolutionizing the recognition of factors critical to spatial and temporal responses to single or multiple environmental cues. Within and pan-species comparisons illuminate deeply conserved circuitry and targets of selection. This knowledge can benefit the breeding and engineering of crops with greater resilience to climate variability and the ability to augment nutrition through plant-microbial interactions.

Nitrate-responsive transcriptome analysis reveals additional genes/processes and associated traits viz. height, tillering, heading date, stomatal density and yield in japonica rice

Our transcriptomic analysis expanded the repertoire of nitrate-responsive genes/processes in rice and revealed their phenotypic association with root/shoot, stomata, tiller, panicle/flowering and yield, with agronomic implications for nitrogen use efficiency. Nitrogen use efficiency (NUE) is a multigenic quantitative trait, involving many N-responsive genes/processes that are yet to be fully characterized. Microarray analysis of early nitrate response in excised leaves of japonica rice revealed 6688 differentially expressed genes (DEGs), including 2640 hitherto unreported across multiple functional categories. They include transporters, enzymes involved in primary/secondary metabolism, transcription factors (TFs), EF-hand containing calcium binding proteins, hormone metabolism/signaling and methytransferases. Some DEGs belonged to hitherto unreported processes viz. alcohol, lipid and trehalose metabolism, mitochondrial membrane organization, protein targeting and stomatal opening. 1158 DEGs were associated with growth physiology and grain yield or phenotypic traits for NUE. We identified seven DEGs for shoot apical meristem, 66 for leaf/culm/root, 31 for tiller, 70 for heading date/inflorescence/spikelet/panicle, 144 for seed and 78 for yield. RT-qPCR validated nitrate regulation of 31 DEGs belonging to various important functional categories/traits. Physiological validation of N-dose responsive changes in plant development revealed that relative to 1.5 mM, 15 mM nitrate significantly increased stomatal density, stomatal conductance and transpiration rate. Further, root/shoot growth, number of tillers and grain yield declined and panicle emergence/heading date delayed, despite increased photosynthetic rate. We report the binding sites of diverse classes of TFs such as WRKY, MYB, HMG etc., in the 1 kb up-stream regions of 6676 nitrate-responsive DEGs indicating their role in regulating nitrate response/NUE. Together, these findings expand the repertoire of genes and processes involved in genomewide nitrate response in rice and reveal their physiological, phenotypic and agronomic implications for NUE.

Validation of a high-confidence regulatory network for gene-to-NUE phenotype in field-grown rice

Nitrogen (N) and Water (W) - two resources critical for crop productivity - are becoming increasingly limited in soils globally. To address this issue, we aim to uncover the gene regulatory networks (GRNs) that regulate nitrogen use efficiency (NUE) - as a function of water availability - in Oryza sativa, a staple for 3.5 billion people. In this study, we infer and validate GRNs that correlate with rice NUE phenotypes affected by N-by-W availability in the field. We did this by exploiting RNA-seq and crop phenotype data from 19 rice varieties grown in a 2x2 N-by-W matrix in the field. First, to identify gene-to-NUE field phenotypes, we analyzed these datasets using weighted gene co-expression network analysis (WGCNA). This identified two network modules ("skyblue" & "grey60") highly correlated with NUE grain yield (NUEg). Next, we focused on 90 TFs contained in these two NUEg modules and predicted their genome-wide targets using the N-and/or-W response datasets using a random forest network inference approach (GENIE3). Next, to validate the GENIE3 TF→target gene predictions, we performed Precision/Recall Analysis (AUPR) using nine datasets for three TFs validated in planta. This analysis sets a precision threshold of 0.31, used to "prune" the GENIE3 network for high-confidence TF→target gene edges, comprising 88 TFs and 5,716 N-and/or-W response genes. Next, we ranked these 88 TFs based on their significant influence on NUEg target genes responsive to N and/or W signaling. This resulted in a list of 18 prioritized TFs that regulate 551 NUEg target genes responsive to N and/or W signals. We validated the direct regulated targets of two of these candidate NUEg TFs in a plant cell-based TF assay called TARGET, for which we also had in planta data for comparison. Gene ontology analysis revealed that 6/18 NUEg TFs - OsbZIP23 (LOC_Os02g52780), Oshox22 (LOC_Os04g45810), LOB39 (LOC_Os03g41330), Oshox13 (LOC_Os03g08960), LOC_Os11g38870, and LOC_Os06g14670 - regulate genes annotated for N and/or W signaling. Our results show that OsbZIP23 and Oshox22, known regulators of drought tolerance, also coordinate W-responses with NUEg. This validated network can aid in developing/breeding rice with improved yield on marginal, low N-input, drought-prone soils.

R2R3-MYBs in Durum Wheat: Genome-Wide Identification, Poaceae-Specific Clusters, Expression, and Regulatory Dynamics Under Abiotic Stresses

MYB transcription factors (TFs) represent one of the biggest TF families in plants, being involved in various specific plant processes, such as responses to biotic and abiotic stresses. The implication of MYB TFs in the tolerance mechanisms to abiotic stress is particularly interesting for crop breeding, since environmental conditions can negatively affect growth and productivity. Wheat is a worldwide-cultivated cereal, and is a major source of plant-based proteins in human food. In particular, durum wheat plays an important role in global food security improvement, since its adaptation to hot and dry conditions constitutes the base for the success of wheat breeding programs in future. In the present study, a genome-wide identification of R2R3-MYB TFs in durum wheat was performed. MYB profile search and phylogenetic analyses based on homology with Arabidopsis and rice MYB TFs led to the identification of 233 R2R3-TdMYB (Triticum durum MYB). Three Poaceae-specific MYB clusters were detected, one of which had never been described before. The expression of eight selected genes under different abiotic stress conditions, revealed that most of them responded especially to salt and drought stress. Finally, gene regulatory network analyses led to the identification of 41 gene targets for three TdR2R3-MYBs that represent novel candidates for functional analyses. This study provides a detailed description of durum wheat R2R3-MYB genes and contributes to a deeper understanding of the molecular response of durum wheat to unfavorable climate conditions.

Plant NIGT1/HRS1/HHO Transcription Factors: Key Regulators with Multiple Roles in Plant Growth, Development, and Stress Responses

The NIGT1/HRS1/HHO transcription factor (TF) family is a new subfamily of the G2-like TF family in the GARP superfamily and contains two conserved domains: the Myb-DNA binding domain and the hydrophobic and globular domain. Some studies showed that NIGT1/HRS1/HHO TFs are involved in coordinating the absorption and utilization of nitrogen and phosphorus. NIGT1/HRS1/HHO TFs also play an important role in plant growth and development and in the responses to abiotic stresses. This review focuses on recent advances in the structural characteristics of the NIGT1/HRS1/HHO TF family and discusses how the roles and functions of the NIGT1/HRS1/HHO TFs operate in terms of in plant growth, development, and stress responses.

Transcriptomic Characterization of Nitrate-Enhanced Stevioside Glycoside Synthesis in Stevia (Stevia rebaudiana) Bertoni

Nitrogen forms (nitrate (NO₃⁻) or ammonium (NH₄⁺)) are vital to plant growth and metabolism. In stevia (Stevia rebaudiana), it is important to assess whether nitrogen forms can influence the synthesis of the high-value terpene metabolites-steviol glycosides (SGs), together with the underlying mechanisms. Field and pot experiments were performed where stevia plants were fertilized with either NO₃⁻ or NH₄⁺ nutrition to the same level of nitrogen. Physiological measurements suggested that nitrogen forms had no significant impact on biomass and the total nitrogen content of stevia leaves, but NO₃⁻-enhanced leaf SGs contents. Transcriptomic analysis identified 397 genes that were differentially expressed (DEGs) between NO₃⁻ and NH₄⁺ treatments. Assessment of the DEGs highlighted the responses in secondary metabolism, particularly in terpenoid metabolism, to nitrogen forms. Further examinations of the expression patterns of SGs synthesis-related genes and potential transcription factors suggested that GGPPS and CPS genes, as well as the WRKY and MYB transcription factors, could be driving N form-regulated SG synthesis. We concluded that NO₃⁻, rather than NH₄⁺, can promote leaf SG synthesis via the NO₃⁻-MYB/WRKY-GGPPS/CPS module. Our study suggests that insights into the molecular mechanism of how SG synthesis can be affected by nitrogen forms.

Sugar and Nitrate Sensing: A Multi-Billion-Year Story

Sugars and nitrate play a major role in providing carbon and nitrogen in plants. Understanding how plants sense these nutrients is crucial, most notably for crop improvement. The mechanisms underlying sugar and nitrate sensing are complex and involve moonlighting proteins such as the nitrate transporter NRT1.1/NFP6.3 or the glycolytic enzyme HXK1. Major components of nutrient signaling, such as SnRK1, TOR, and HXK1, are relatively well conserved across eukaryotes, and the diversification of components such as the NRT1 family and the SWEET sugar transporters correlates with plant terrestrialization. In plants, Tre6P plays a hormone-like role in plant development. In addition, nutrient signaling has evolved to interact with the more recent hormone signaling, allowing fine-tuning of physiological and developmental responses.

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.

Gene regulatory network and its constituent transcription factors that control nitrogen-deficiency responses in rice

Increase in the nitrogen (N)-use efficiency and optimization of N response in crop species are urgently needed. Although transcription factor-based genetic engineering is a promising approach for achieving these goals, transcription factors that play key roles in the response to N deficiency have not been studied extensively. Here, we performed RNA-seq analysis of root samples of 20 Asian rice (Oryza sativa) accessions with differential nutrient uptake. Data obtained from plants exposed to N-replete and N-deficient conditions were subjected to coexpression analysis and machine learning-based pathway inference to dissect the gene regulatory network required for the response to N deficiency. Four transcription factors, including members of the G2-like and bZIP families, were predicted to function as key regulators of gene transcription within the network in response to N deficiency. Cotransfection assays validated inferred novel regulatory pathways, and further analyses using genome-edited knockout lines suggested that these transcription factors are important for N-deficiency responses in planta. Many of the N deficiency-responsive genes, including those encoding key regulators within the network, were coordinately regulated by transcription factors belonging to different families. Transcription factors identified in this study could be valuable for the modification of N response and metabolism.

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

Nitrate (N) response is modulated by light, but not understood from a genome-wide perspective. Comparative transcriptomic analyses of nitrate response in light-grown and etiolated rice leaves revealed 303 and 249 differentially expressed genes (DEGs) respectively. A majority of them were exclusive to light (270) or dark (216) condition, whereas 33 DEGs were common. The latter may constitute response to N signaling regardless of light. Functional annotation and pathway enrichment analyses of the DEGs showed that nitrate primarily modulates conserved N signaling and metabolism in light, whereas oxidation–reduction processes, pentose-phosphate shunt, starch-, sucrose- and glycerolipid-metabolisms in the dark. Differential N-regulation of these pathways by light could be attributed to the involvement of distinctive sets of transporters, transcription factors, enriched cis-acting motifs in the promoters of DEGs as well as differential modulation of N-responsive transcriptional regulatory networks in light and dark. Sub-clustering of DEGs-associated protein–protein interaction network constructed using experimentally validated interactors revealed that nitrate regulates a molecular complex consisting of nitrite reductase, ferredoxin-NADP reductase and ferredoxin. This complex is associated with flowering time, revealing a meeting point for N-regulation of N-response and N-use efficiency. Together, our results provide novel insights into distinct pathways of N-signaling in light and dark conditions.

Advancing from protein interactomes and gene co-expression networks towards multi-omics-based composite networks: approaches for predicting and extracting biological knowledge

Prediction of biological interaction networks from single-omics data has been extensively implemented to understand various aspects of biological systems. However, more recently, there is a growing interest in integrating multi-omics datasets for the prediction of interactomes that provide a global view of biological systems with higher descriptive capability, as compared to single omics. In this review, we have discussed various computational approaches implemented to infer and analyze two of the most important and well studied interactomes: protein-protein interaction networks and gene co-expression networks. We have explicitly focused on recent methods and pipelines implemented to infer and extract biologically important information from these interactomes, starting from utilizing single-omics data and then progressing towards multi-omics data. Accordingly, recent examples and case studies are also briefly discussed. Overall, this review will provide a proper understanding of the latest developments in protein and gene network modelling and will also help in extracting practical knowledge from them.

A Tripartite Interaction among the Basidiomycete Rhodotorula mucilaginosa, N2-Fixing Endobacteria, and Rice Improves Plant Nitrogen Nutrition

Nitrogen (N) limits crop yield, and improvement of N nutrition remains a key goal for crop research; one approach to improve N nutrition is identifying plant-interacting, N2-fixing microbes. Rhodotorula mucilaginosa JGTA-S1 is a basidiomycetous yeast endophyte of narrowleaf cattail (Typha angustifolia). JGTA-S1 could not convert nitrate or nitrite to ammonium but harbors diazotrophic (N2-fixing) endobacteria (Pseudomonas stutzeri) that allow JGTA-S1 to fix N2 and grow in a N-free environment; moreover, P. stutzeri dinitrogen reductase was transcribed in JGTA-S1 even under adequate N. Endobacteria-deficient JGTA-S1 had reduced fitness, which was restored by reintroducing P. stutzeri JGTA-S1 colonizes rice (Oryza sativa), significantly improving its growth, N content, and relative N-use efficiency. Endofungal P. stutzeri plays a significant role in increasing the biomass and ammonium content of rice treated with JGTA-S1; also, JGTA-S1 has better N2-fixing ability than free-living P. stutzeri and provides fixed N to the plant. Genes involved in N metabolism, N transporters, and NODULE INCEPTION-like transcription factors were upregulated in rice roots within 24 h of JGTA-S1 treatment. In association with rice, JGTA-S1 has a filamentous phase and P. stutzeri only penetrated filamentous JGTA-S1. Together, these results demonstrate an interkingdom interaction that improves rice N nutrition.

QTL mapping for maize starch content and candidate gene prediction combined with co-expression network analysis

A major QTL Qsta9.1 was identified on chromosome 9, combined with GWAS, and co-expression network analysis showed that GRMZM2G110929 and GRMZM5G852704 are the potential candidates for association with maize kernel starch content. Increasing maize kernel starch content may not only lead to higher maize kernel yields and qualities, but also help meet industry demands. By using the intermated B73 × Mo17 population, QTLs were mapped for starch content in this study. A major QTL Qsta9.1 was detected in a 1.7 Mb interval on chromosome 9 and validated by allele frequency analysis in extreme tails of a newly constructed segregating population. According to genome-wide association study (GWAS) based on genotyping of a natural population, we identified a significant SNP for starch content within the ORF region of GRMZM5G852704_T01 colocalized with QTL Qsta9.1. Co-expression network analysis was also conducted, and 28 modules were constructed during six seed developmental stages. Functional enrichment was performed for each module, and one module showed the most possibility for the association with carbohydrate-related processes. In this module, one transcripts GRMZM2G110929_T01 located in the Qsta9.1 assigned 1.7 Mb interval encoding GLABRA2 expression modulator. Its expression level in B73 was lower than that in Mo17 across all seed developmental stages, implying the possibility for the candidate gene of Qsta9.1. Our studies combined GWAS, mRNA profiling, and traditional QTL analyses to identify a major locus for controlling seed starch content in maize.

Rice Genome-Scale Network Integration Reveals Transcriptional Regulators of Grass Cell Wall Synthesis

Grasses have evolved distinct cell wall composition and patterning relative to dicotyledonous plants. However, despite the importance of this plant family, transcriptional regulation of its cell wall biosynthesis is poorly understood. To identify grass cell wall-associated transcription factors, we constructed the Rice Combined mutual Ranked Network (RCRN). The RCRN covers >90% of annotated rice (Oryza sativa) genes, is high quality, and includes most grass-specific cell wall genes, such as mixed-linkage glucan synthases and hydroxycinnamoyl acyltransferases. Comparing the RCRN and an equivalent Arabidopsis network suggests that grass orthologs of most genetically verified eudicot cell wall regulators also control this process in grasses, but some transcription factors vary significantly in network connectivity between these divergent species. Reverse genetics, yeast-one-hybrid, and protoplast-based assays reveal that OsMYB61a activates a grass-specific acyltransferase promoter, which confirms network predictions and supports grass-specific cell wall synthesis genes being incorporated into conserved regulatory circuits. In addition, 10 of 15 tested transcription factors, including six novel Wall-Associated regulators (WAP1, WACH1, WAHL1, WADH1, OsMYB13a, and OsMYB13b), alter abundance of cell wall-related transcripts when transiently expressed. The results highlight the quality of the RCRN for examining rice biology, provide insight into the evolution of cell wall regulation, and identify network nodes and edges that are possible leads for improving cell wall composition.

Early molecular events associated with nitrogen deficiency in rice seedling roots

Nitrogen (N) deficiency is one of the most common problems in rice. The symptoms of N deficiency are well documented, but the underlying molecular mechanisms are largely unknown in rice. Here, we studied the early molecular events associated with N starvation (−N, 1 h), focusing on amino acid analysis and identification of −N-regulated genes in rice roots. Interestingly, levels of glutamine rapidly decreased within 15 min of −N treatment, indicating that part of the N-deficient signals could be mediated by glutamine. Transcriptome analysis revealed that genes involved in metabolism, plant hormone signal transduction (e.g. abscisic acid, auxin, and jasmonate), transporter activity, and oxidative stress responses were rapidly regulated by −N. Some of the −N-regulated genes encode transcription factors, protein kinases and protein phosphatases, which may be involved in the regulation of early −N responses in rice roots. Previously, we used similar approaches to identify glutamine-, glutamate-, and ammonium nitrate-responsive genes. Comparisons of the genes induced by different forms of N with the −N-regulated genes identified here have provided a catalog of potential N regulatory genes for further dissection of the N signaling pathwys in rice.

Allelic variants of OsSUB1A cause differential expression of transcription factor genes in response to submergence in rice

BACKGROUND

Flooding during seasonal monsoons affects millions of hectares of rice-cultivated areas across Asia. Submerged rice plants die within a week due to lack of oxygen, light and excessive elongation growth to escape the water. Submergence tolerance was first reported in an aus-type rice landrace, FR13A, and the ethylene-responsive transcription factor (TF) gene SUB1A-1 was identified as the major tolerance gene. Intolerant rice varieties generally lack the SUB1A gene but some intermediate tolerant varieties, such as IR64, carry the allelic variant SUB1A-2. Differential effects of the two alleles have so far not been addressed. As a first step, we have therefore quantified and compared the expression of nearly 2500 rice TF genes between IR64 and its derived tolerant near isogenic line IR64-Sub1, which carries the SUB1A-1 allele. Gene expression was studied in internodes, where the main difference in expression between the two alleles was previously shown.

RESULTS

Nineteen and twenty-six TF genes were identified that responded to submergence in IR64 and IR64-Sub1, respectively. Only one gene was found to be submergence-responsive in both, suggesting different regulatory pathways under submergence in the two genotypes. These differentially expressed genes (DEGs) mainly included MYB, NAC, TIFY and Zn-finger TFs, and most genes were downregulated upon submergence. In IR64, but not in IR64-Sub1, SUB1B and SUB1C, which are also present in the Sub1 locus, were identified as submergence responsive. Four TFs were not submergence responsive but exhibited constitutive, genotype-specific differential expression. Most of the identified submergence responsive DEGs are associated with regulatory hormonal pathways, i.e. gibberellins (GA), abscisic acid (ABA), and jasmonic acid (JA), apart from ethylene. An in-silico promoter analysis of the two genotypes revealed the presence of allele-specific single nucleotide polymorphisms, giving rise to ABRE, DRE/CRT, CARE and Site II cis-elements, which can partly explain the observed differential TF gene expression.

CONCLUSION

This study identified new gene targets with the potential to further enhance submergence tolerance in rice and provides insights into novel aspects of SUB1A-mediated tolerance.

Identification of early ammonium nitrate-responsive genes in rice roots

Ammonium has long been used as the predominant form of nitrogen source for paddy rice (Oryza sativa). Recently, increasing evidence suggests that nitrate also plays an important role for nitrogen acquisition in the rhizosphere of waterlogged paddy rice. Ammonium and nitrate have a synergistic effect on promoting rice growth. However, the molecular responses induced by simultaneous treatment with ammonium and nitrate have been less studied in rice. Here, we performed transcriptome analysis to identify genes that are rapidly regulated by ammonium nitrate (1.43 mM, 30 min) in rice roots. The combination of ammonium and nitrate preferentially induced the expression of nitrate-responsive genes. Gene ontology enrichment analysis revealed that the early ammonium nitrate-responsive genes were enriched in "regulation of transcription, DNA-dependent" and "protein amino acid phosphorylation" indicating that some of the genes identified in this study may play an important role in nitrogen sensing and signaling. Several defense/stress-responsive genes, including some encoding transcription factors and mitogen-activated protein kinase kinase kinases, were also rapidly induced by ammonium nitrate. These results suggest that nitrogen metabolism, signaling, and defense/stress responses are interconnected. Some of the genes identified here may be involved in the interaction of nitrogen signaling and defense/stress-response pathways in plants.

Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks

Nutrient signalling integrates and coordinates gene expression, metabolism and growth. However, its primary molecular mechanisms remain incompletely understood in plants and animals. Here we report unique Ca²⁺ signalling triggered by nitrate with live imaging of an ultrasensitive biosensor in Arabidopsis leaves and roots. A nitrate-sensitized and targeted functional genomic screen identifies subgroup III Ca²⁺-sensor protein kinases (CPKs) as master regulators that orchestrate primary nitrate responses. A chemical switch with the engineered mutant CPK10(M141G) circumvents embryo lethality and enables conditional analyses of cpk10 cpk30 cpk32 triple mutants to define comprehensive nitrate-associated regulatory and developmental programs. Nitrate-coupled CPK signalling phosphorylates conserved NIN-LIKE PROTEIN (NLP) transcription factors to specify the reprogramming of gene sets for downstream transcription factors, transporters, nitrogen assimilation, carbon/nitrogen metabolism, redox, signalling, hormones and proliferation. Conditional cpk10 cpk30 cpk32 and nlp7 mutants similarly impair nitrate-stimulated system-wide shoot growth and root establishment. The nutrient-coupled Ca²⁺ signalling network integrates transcriptome and cellular metabolism with shoot-root coordination and developmental plasticity in shaping organ biomass and architecture.

EGRINs (Environmental Gene Regulatory Influence Networks) in Rice That Function in the Response to Water Deficit, High Temperature, and Agricultural Environments

Environmental gene regulatory influence networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental signals. EGRINs encompass many layers of regulation, which culminate in changes in accumulated transcript levels. Here, we inferred EGRINs for the response of five tropical Asian rice (Oryza sativa) cultivars to high temperatures, water deficit, and agricultural field conditions by systematically integrating time-series transcriptome data, patterns of nucleosome-free chromatin, and the occurrence of known cis-regulatory elements. First, we identified 5447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes harboring known cis-regulatory motifs in nucleosome-free regions proximal to their transcriptional start sites. We then used network component analysis to estimate the regulatory activity for each TF based on the expression of its putative target genes. Finally, we inferred an EGRIN using the estimated transcription factor activity (TFA) as the regulator. The EGRINs include regulatory interactions between 4052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of the heat shock factor family, including a putative regulatory connection between abiotic stress and the circadian clock. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference.

Tools of the trade: studying molecular networks in plants

Driven by recent technological improvements, genes can be now studied in a larger biological context. Genes and their protein products rarely operate as a single entity and large-scale mapping by protein-protein interactions can unveil the molecular complexes that form in the cell to carry out various functions. Expression analysis under multiple conditions, supplemented with protein-DNA binding data can highlight when genes are active and how they are regulated. Representing these data in networks and finding strongly connected sub-graphs has proven to be a powerful tool to predict the function of unknown genes. As such networks are gradually becoming available for various plant species, it becomes possible to study how networks evolve. This review summarizes currently available network data and related tools for plants. Furthermore we aim to provide an outlook of future analyses that can be done in plants based on work done in other fields.

Learning from Co-expression Networks: Possibilities and Challenges

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.