Metabolomics is an emerging branch of “omics” and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment–gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Increasing agricultural productivity via modern breeding strategies is of prime interest to attain global food security. An array of biotic and abiotic stressors affect productivity as well as the quality of crop plants, and it is a primary need to develop crops with improved adaptability, high productivity, and resilience against these biotic/abiotic stressors. Conventional approaches to genetic engineering involve tedious procedures. State-of-the-art OMICS approaches reinforced with next-generation sequencing and the latest developments in genome editing tools have paved the way for targeted mutagenesis, opening new horizons for precise genome engineering. Various genome editing tools such as transcription activator-like effector nucleases (TALENs), zinc-finger nucleases (ZFNs), and meganucleases (MNs) have enabled plant scientists to manipulate desired genes in crop plants. However, these approaches are expensive and laborious involving complex procedures for successful editing. Conversely, CRISPR/Cas9 is an entrancing, easy-to-design, cost-effective, and versatile tool for precise and efficient plant genome editing. In recent years, the CRISPR/Cas9 system has emerged as a powerful tool for targeted mutagenesis, including single base substitution, multiplex gene editing, gene knockouts, and regulation of gene transcription in plants. Thus, CRISPR/Cas9-based genome editing has demonstrated great potential for crop improvement but regulation of genome-edited crops is still in its infancy. Here, we extensively reviewed the availability of CRISPR/Cas9 genome editing tools for plant biotechnologists to target desired genes and its vast applications in crop breeding research.

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Nitrogen (N) is an essential element for plant growth and development. The application of a balanced and optimal amount of N is required for sustainable plant yield. For this, different N sources and forms are used, that including ammonium (NH4+) and nitrate (NO3−). These are the main sources for N uptake by plants where NH4+/NO3− ratios have a significant effect on the biomass, quality and metabolites composition of lettuce grown in soil, substrate and hydroponic cultivation systems. A limited supply of N resulted in the reduction in the biomass, quality and overall yield of lettuce. Additionally, different types of metabolites were produced with varying concentrations of N sources and can be used as metabolic markers to improve the N use efficiency. To investigate the differential metabolic activity, we planted lettuce with different NH4+/NO3− ratios (100:0, 75:25, 50:50, 25:75 and 0:100%) and a control (no additional N applied) in soil, substrate and hydroponic cultivation systems. The results revealed that the 25% NH4+/75% NO3− ratio increased the relative chlorophyll contents as well as the biomass of lettuce in all cultivation systems. However, lettuce grown in the hydroponic cultivation system showed the best results. The concentration of essential amino acids including alanine, valine, leucine, lysine, proline and serine increased in soil and hydroponically grown lettuce treated with the 25% NH4+/75% NO3− ratio. The taste and quality-related compounds in lettuce showed maximum relative abundance with the 25% NH4+/75% NO3− ratio, except ascorbate (grown in soil) and lactupicrin (grown in substrate), which showed maximum relative abundance in the 50% NH4+/50% NO3− ratio and control treatments, respectively. Moreover, 1-O-caffeoylglucose, 1,3-dicaffeoylquinic acid, aesculetin and quercetin-3-galactoside were increased by the application of the 100% NH4+/0% NO3− ratio in soil-grown lettuce. The 25% NH4+/75% NO3− ratio was more suitable in the hydroponic cultivation system to obtain increased lettuce biomass. The metabolic profiling of lettuce showed different behaviors when applying different NH4+/NO3− ratios. Therefore, the majority of the parameters were largely influenced by the 25% NH4+/75% NO3− ratio, which resulted in the hyper-accumulation of health-promoting compounds in lettuce. In conclusion, the optimal N applications improve the quality of lettuce grown in soil, substrate and hydroponic cultivation systems which ultimately boost the nutritional value of lettuce.

Climate change continues to threaten global crop output by reducing annual productivity. As a result, global food security is now considered as one of the most important challenges facing humanity. To address this challenge, modern crop breeding approaches are required to create plants that can cope with increased abiotic/biotic stress. Metabolomics is rapidly gaining traction in plant breeding by predicting the metabolic marker for plant performance under a stressful environment and has emerged as a powerful tool for guiding crop improvement. The advent of more sensitive, automated, and high-throughput analytical tools combined with advanced bioinformatics and other omics techniques has laid the foundation to broadly characterize the genetic traits for crop improvement. Progress in metabolomics allows scientists to rapidly map specific metabolites to the genes that encode their metabolic pathways and offer plant scientists an excellent opportunity to fully explore and rationally harness the wealth of metabolites that plants biosynthesize. Here, we outline the current application of advanced metabolomics tools integrated with other OMICS techniques that can be used to: dissect the details of plant genotype–metabolite–phenotype interactions facilitating metabolomics-assisted plant breeding for probing the stress-responsive metabolic markers, explore the hidden metabolic networks associated with abiotic/biotic stress resistance, facilitate screening and selection of climate-smart crops at the metabolite level, and enable accurate risk-assessment and characterization of gene edited/transgenic plants to assist the regulatory process. The basic concept behind metabolic editing is to identify specific genes that govern the crucial metabolic pathways followed by the editing of one or more genes associated with those pathways. Thus, metabolomics provides a superb platform for not only rapid assessment and commercialization of future genome-edited crops, but also for accelerated metabolomics-assisted plant breeding. Furthermore, metabolomics can be a useful tool to expedite the crop research if integrated with speed breeding in future.

In precision agriculture, the nitrogen level is significantly important for establishing phenotype, quality and yield of crops. It cannot be achieved in the future without appropriate nitrogen fertilizer application. Moreover, a convenient and real-time advance technology for nitrogen nutrition diagnosis of crops is a prerequisite for an efficient and reasonable nitrogen-fertilizer management system. With the development of research on plant phenotype and artificial intelligence technology in agriculture, deep learning has demonstrated a great potential in agriculture for recognizing nondestructive nitrogen nutrition diagnosis in plants by automation and high throughput at a low cost. To build a nitrogen nutrient-diagnosis model, muskmelons were cultivated under different nitrogen levels in a greenhouse. The digital images of canopy leaves and the environmental factors (light and temperature) during the growth period of muskmelons were tracked and analyzed. The nitrogen concentrations of the plants were measured, we successfully constructed and trained machine-learning- and deep-learning models based on the traditional backpropagation neural network (BPNN), the emerging convolution neural network (CNN), the deep convolution neural network (DCNN) and the long short-term memory (LSTM) for the nitrogen nutrition diagnosis of muskmelon. The adjusted determination coefficient (R2) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R2 = 0.567 and MSE = 0.429 for BPNN model; R2 = 0.376 and MSE = 0.628 for CNN model; R2 = 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R2 = 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

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