Aluminum (Al) toxicity is one of the major limitations that inhibit plant growth and development in acidic soils. In acidic soils (pH < 5.0), phototoxic-aluminum (Al³⁺) rapidly inhibits root growth, and subsequently affects water and nutrient uptake in plants. This review updates the existing knowledge concerning the role of mineral nutrition for alleviating Al toxicity in plants to acid soils. Here, we explored phosphorus (P) is more beneficial in plants under P-deficient, and Al toxic conditions. Exogenous P addition increased root respiration, plant growth, chlorophyll content, and dry matter yield. Calcium (Ca) amendment (liming) is effective for correcting soil acidity, and for alleviating Al toxicity. Magnesium (Mg) is able to prevent Al migration through the cytosolic plasma membrane in root tips. Sulfur (S) is recognized as a versatile element that alleviates several metals toxicity including Al. Moreover, silicon (Si), and other components such as industrial byproducts, hormones, organic acids, polyamines, biofertilizers, and biochars played promising roles for mitigating Al toxicity in plants. Furthermore, this review provides a comprehensive understanding of several new methods and low-cost effective strategies relevant to the exogenous application of mineral nutrition on Al toxicity mitigation. This information would be effective for further improvement of crop plants in acid soils.

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

Proanthocyanidins (PAs) are oligomeric flavonoids and one group of end products of the phenylpropanoid pathway. PAs have been reported to be beneficial for human and animal health and are particularly important in pastoral agricultural systems for improved animal production and reduced greenhouse gas emissions. However, the main forage legumes grown in these systems, such as Trifolium repens and Medicago sativa, do not contain any substantial amounts of PAs in leaves. We have identified from the foliar PA-accumulating legume Trifolium arvense an R2R3-MYB transcription factor, TaMYB14, and provide evidence that this transcription factor is involved in the regulation of PA biosynthesis in legumes. TaMYB14 expression is necessary and sufficient to up-regulate late steps of the phenylpropanoid pathway and to induce PA biosynthesis. RNA interference silencing of TaMYB14 resulted in almost complete cessation of PA biosynthesis in T. arvense, whereas Nicotiana tabacum, M. sativa, and T. repens plants constitutively expressing TaMYB14 synthesized and accumulated PAs in leaves up to 1.8% dry matter. Targeted liquid chromatography-multistage tandem mass spectrometry analysis identified foliar PAs up to degree of polymerization 6 in leaf extracts. Hence, genetically modified M. sativa and T. repens plants expressing TaMYB14 provide a viable option for improving animal health and mitigating the negative environmental impacts of pastoral animal production systems.

Napier grass (Pennisetum purpureum Schumach.) is a fast-growing perennial grass native to Sub-Saharan Africa that is widely grown across the tropical and subtropical regions of the world. It is a multipurpose forage crop, primarily used to feed cattle in cut and carry feeding systems. Characterization and diversity studies on a small collection of Napier grasses have identified a moderate level of genetic variation and highlighted the availability of some good agronomic traits, particularly high biomass production, as a forage crop. However, very little information exists on precise phenotyping, genotyping and the application of molecular technologies to Napier grass improvement using modern genomic tools which have been applied in advancing the selection and breeding of important food crops. In this review paper, existing information on genetic resources, molecular diversity, yield and nutritional quality of Napier grass will be discussed. Recent findings on characterizing disease resistance and abiotic stress (drought) tolerance will also be highlighted. Finally, opportunities and future prospects for better conservation and use arising from the application of modern genomic tools in Napier grass phenotyping and genotyping will be discussed.

Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure–Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson’s correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R² ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R² = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.