Integrative Transcriptomic and Proteomic Analysis Reveals an Alternative Molecular Network of Glutamine Synthetase 2 Corresponding to Nitrogen Deficiency in Rice (Oryza sativa L.)

Effects of Biological Nitrogen Metabolism on Glufosinate-Susceptible and -Resistant Goosegrass (Eleusine indica L.)

Glufosinate is a broad-spectrum herbicide used to control most weeds in agriculture worldwide. Goosegrass (Eleusine indica L.) is one of the top ten malignant weeds across the world, showing high tolerance to glufosinate via different mechanisms that are not yet fully understood. This study revealed that nitrogen metabolism could be a target-resistant site, providing clues to finally clarify the mechanism of glufosinate resistance in resistant goosegrass populations. Compared to susceptible goosegrass (NX), the resistant goosegrass (AUS and CS) regarding the stress of glufosinate showed stronger resistance with lower ammonia contents, higher target enzyme GS (glutamine synthetase) activity, and lower GOGAT (glutamine 2-oxoglutarate aminotransferase) activity. The GDH (glutamate dehydrogenase) activity of another pathway increased, but its gene expression was downregulated in resistant goosegrass (AUS). Analyzing the transcriptome and proteome data of goosegrass under glufosinate stress at 36 h showed that the KEGG pathway of the nitrogen metabolism was enriched in glufosinate-susceptible goosegrass (NX), but not in glufosinate-resistant goosegrass (CS and AUS). Several putative target genes involved in glufosinate stress countermeasures were identified. This study provides specific insights into the nitrogen metabolism of resistant goosegrass, and gives a basis for future functional verification of glufosinate-tolerance genes in plants.

Physiological and transcriptome analysis of Dendrobium officinale under low nitrogen stress

Nitrogen (N) is the main nutrient of plants, and low nitrogen usually affects plant growth and crop yield. The traditional Chinese herbal medicine Dendrobium officinale Kimura et. Migo is a typical low nitrogen-tolerant plant, and its mechanism in response to low nitrogen stress has not previously been reported. In this study, physiological measurements and RNA-Seq analysis were used to analyse the physiological changes and molecular responses of D. officinale under different nitrogen concentrations. The results showed that under low nitrogen levels, the growth, photosynthesis and superoxide dismutase activity were found to be significantly inhibited, while the activities of peroxidase and catalase, the content of polysaccharides and flavonoids significantly increased. Differentially expressed genes (DEGs) analysis showed that nitrogen and carbon metabolisms, transcriptional regulation, antioxidative stress, secondary metabolite synthesis and signal transduction all made a big difference in low nitrogen stress. Therefore, copious polysaccharide accumulation, efficient assimilation and recycling of nitrogen, as well as rich antioxidant components play critical roles. This study is helpful for understanding the response mechanism of D. officinale to low nitrogen levels, which might provide good guidance for practical production of high quality D. officinale .

Coregulation of glutamine synthetase1;2 (GLN1;2) and NADH-dependent glutamate synthase (GLT1) gene expression in Arabidopsis roots in response to ammonium supply

Ammonium absorbed by roots is assimilated into amino acids. The glutamine synthetase/glutamate synthase (glutamine 2-oxoglutarate aminotransferase) (GS/GOGAT) cycle is essential to this biological process. In Arabidopsis thaliana, GLN1;2 and GLT1 are the GS and GOGAT isoenzymes induced in response to ammonium supply and playing key roles in ammonium utilization. Although recent studies suggest gene regulatory networks involved in transcriptional regulation of ammonium-responsive genes, direct regulatory mechanisms for ammonium-induced expression of GS/GOGAT remain unclear. In this study, we revealed that the expression of GLN1;2 and GLT1 in Arabidopsis is not directly induced by ammonium but is regulated by glutamine or post-glutamine metabolites produced by ammonium assimilation. Previously, we identified a promoter region required for ammonium-responsive expression of GLN1;2. In this study, we further dissected the ammonium-responsive region of the GLN1;2 promoter and also performed a deletion analysis of the GLT1 promoter, which led to the identification of a conserved ammonium-responsive region. Yeast one-hybrid screening using the ammonium-responsive region of the GLN1;2 promoter as a decoy sequence revealed a trihelix family transcription factor DF1 that binds to this region. A putative DF1 binding site was also found in the ammonium-responsive region of the GLT1 promoter.

Single-amplified genomes reveal most streamlined free-living marine bacteria

Evolutionary adaptations of prokaryotes to the environment sometimes result in genome reduction. Our knowledge of this phenomenon among free-living bacteria remains scarce. We address the dynamics and limits of genome reduction by examining one of the most abundant bacteria in the ocean, the SAR86 clade. Despite its abundance, comparative genomics has been limited by the absence of pure cultures and the poor representation in metagenome-assembled genomes. We co-assembled multiple previously available single-amplified genomes to obtain the first complete genomes from members of the four families. All families showed a convergent evolutionary trajectory with characteristic features of streamlined genomes, most pronounced in the TMED112 family. This family has a genome size of ca. 1 Mb and only 1 bp as median intergenic distance, exceeding values found in other abundant microbes such as SAR11, OM43 and Prochlorococcus. This genomic simplification led to a reduction in the biosynthesis of essential molecules, DNA repair-related genes, and the ability to sense and respond to environmental factors, which could suggest an evolutionary dependence on other co-occurring microbes for survival (Black Queen hypothesis). Therefore, these reconstructed genomes within the SAR86 clade provide new insights into the limits of genome reduction in free-living marine bacteria.

Low nitrogen stress-induced transcriptome changes revealed the molecular response and tolerance characteristics in maintaining the C/N balance of sugar beet (Beta vulgaris L.)

Nitrogen (N) is an essential macronutrient for plants, acting as a common limiting factor for crop yield. The application of nitrogen fertilizer is related to the sustainable development of both crops and the environment. To further explore the molecular response of sugar beet under low nitrogen (LN) supply, transcriptome analysis was performed on the LN-tolerant germplasm '780016B/12 superior'. In total, 580 differentially expressed genes (DEGs) were identified in leaves, and 1,075 DEGs were identified in roots (log₂ ^|FC| ≥ 1; q value < 0.05). Gene Ontology (GO), protein-protein interaction (PPI), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses clarified the role and relationship of DEGs under LN stress. Most of the downregulated DEGs were closely related to "photosynthesis" and the metabolism of "photosynthesis-antenna proteins", "carbon", "nitrogen", and "glutathione", while the upregulated DEGs were involved in flavonoid and phenylalanine biosynthesis. For example, GLUDB (glutamate dehydrogenase B) was identified as a key downregulated gene, linking carbon, nitrogen, and glutamate metabolism. Thus, low nitrogen-tolerant sugar beet reduced energy expenditure mainly by reducing the synthesis of energy-consuming amino acids, which in turn improved tolerance to low nitrogen stress. The glutathione metabolism biosynthesis pathway was promoted to quench reactive oxygen species (ROS) and protect cells from oxidative damage. The expression levels of nitrogen assimilation and amino acid transport genes, such as NRT2.5 (high-affinity nitrate transporter), NR (nitrate reductase [NADH]), NIR (ferredoxin-nitrite reductase), GS (glutamine synthetase leaf isozyme), GLUDB, GST (glutathione transferase) and GGT3 (glutathione hydrolase 3) at low nitrogen levels play a decisive role in nitrogen utilization and may affect the conversion of the carbon skeleton. DFRA (dihydroflavonol 4-reductase) in roots was negatively correlated with NIR in leaves (coefficient = -0.98, p < 0.05), suggesting that there may be corresponding remote regulation between "flavonoid biosynthesis" and "nitrogen metabolism" in roots and leaves. FBP (fructose 1,6-bisphosphatase) and PGK (phosphoglycerate kinase) were significantly positively correlated (p < 0.001) with Ci (intercellular CO₂ concentration). The reliability and reproducibility of the RNA-seq data were further confirmed by real-time fluorescence quantitative PCR (qRT-PCR) validation of 22 genes (R² = 0.98). This study reveals possible pivotal genes and metabolic pathways for sugar beet adaptation to nitrogen-deficient environments.

Integration of multi-omics technologies for crop improvement: Status and prospects

With the rapid development of next-generation sequencing (NGS), multi-omics techniques have been emerging as effective approaches for crop improvement. Here, we focus mainly on addressing the current status and future perspectives toward omics-related technologies and bioinformatic resources with potential applications in crop breeding. Using a large amount of omics-level data from the functional genome, transcriptome, proteome, epigenome, metabolome, and microbiome, clarifying the interaction between gene and phenotype formation will become possible. The integration of multi-omics datasets with pan-omics platforms and systems biology could predict the complex traits of crops and elucidate the regulatory networks for genetic improvement. Different scales of trait predictions and decision-making models will facilitate crop breeding more intelligent. Potential challenges that integrate the multi-omics data with studies of gene function and their network to efficiently select desirable agronomic traits are discussed by proposing some cutting-edge breeding strategies for crop improvement. Multi-omics-integrated approaches together with other artificial intelligence techniques will contribute to broadening and deepening our knowledge of crop precision breeding, resulting in speeding up the breeding process.

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R 2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.