Cereals account for a large proportion of the human diet and are an important source of protein. The preparation of cereal protein peptides is a good way to utilize these proteins. Cereal protein peptides have good application potential as antioxidant, antibacterial, anti-inflammatory and anticancer compounds, in lowering blood pressure, controlling blood sugar, and inhibiting thrombosis. This article reviews the literature on the functional properties, mechanisms of action, and applications of cereal protein peptides in the food industry with two perspectives, and summarizes the methods for their preparation and identification. The biologically active peptides derived from different grain proteins have varied main functional properties, which may be related to the differences in the amino acid composition and protein types of different grains. On this basis, the structure-activity relationship of cereal protein peptides was discussed. The advancement of identification technology makes the integration of bioinformatics and bioactive peptide research closer. Bioinformatics by combination of online database, computer simulation and experimental verification is helpful to in-deep study the structure-activity relationship of biologically active peptides, and improve efficiency in the process of obtaining target peptides with less cost. In addition, the application of cereal protein peptides in the food industry is also discussed.

As one of the oldest agricultural crops in China, millet (Panicum miliaceum) has powerful drought tolerance. In this study, transcriptome and metabolome analyses of ‘Hequ Red millet’ (HQ) and ‘Yanshu No.10’ (YS10) millet after 6 h of drought stress were performed. Transcriptome characteristics of drought stress in HQ and YS10 were characterized by Pacbio full-length transcriptome sequencing. The pathway analysis of the differentially expressed genes (DEGs) showed that the highly enriched categories were related to starch and sucrose metabolism, pyruvate metabolism, metabolic pathways, and the biosynthesis of secondary metabolites when the two millet varieties were subjected to drought stress. Under drought stress, 245 genes related to energy metabolism were found to show significant changes between the two strains. Further analysis showed that 219 genes related to plant hormone signal transduction also participated in the drought response. In addition, numerous genes involved in anthocyanin metabolism and photosynthesis were confirmed to be related to drought stress, and these genes showed significant differential expression and played an important role in anthocyanin metabolism and photosynthesis. Moreover, we identified 496 transcription factors related to drought stress, which came from 10 different transcription factor families, such as bHLH, C3H, MYB, and WRKY. Further analysis showed that many key genes related to energy metabolism, such as citrate synthase, isocitrate dehydrogenase, and ATP synthase, showed significant upregulation, and most of the structural genes involved in anthocyanin biosynthesis also showed significant upregulation in both strains. Most genes related to plant hormone signal transduction showed upregulated expression, while many JA and SA signaling pathway-related genes were downregulated. Metabolome analysis was performed on ‘Hequ red millet’ (HQ) and ‘Yanshu 10’ (YS10), a total of 2082 differential metabolites (DEMs) were identified. These findings indicate that energy metabolism, anthocyanins, photosynthesis, and plant hormones are closely related to the drought resistance of millet and adapt to adversity by precisely regulating the levels of various molecular pathways.

Increase of planting density has been widely used to increase grain yield in crops. However, it may lead to higher risk of lodging hence causing significant yield loss of the crop. To investigate the effects of planting density on lodging-related morphology, lodging rate (LR), and yield of tartary buckwheat, an experiment was carried out with a split-plot randomized block design at the experimental farm of Chengdu University (Sichuan, China) in the 2012 and 2013 growing seasons. Results showed that plant density significantly affected characteristics of stem and root. In each season, with the increasing of planting density, light transmittance, main root length, number of first lateral root, root volume, internode number, and first internode diameter decreased, the plant height, first internode length, abortion rate and LR increased. Increasing density caused decreased grains number per plant, the dry matter weight and yield displayed an acceleration first and then deceleration. The correlation analysis indicated that the internode number, first internode diameter, number of first lateral roots, and root volume were significantly negatively correlated with LR, but positively correlated with stem breaking strength and lodging resistance index. The LR was significantly positively correlated with plant height and first internode length. In both years, the D2 (9 × 10⁵ plant ha⁻¹) and D3 (12 × 10⁵ plant ha⁻¹) yielded more grains than in other treatments, and the effects of density on two cultivars showed the same trend. The results suggested that planting density could alter lodging-related traits, lodging resistance, and yield of tartary buckwheat.

Soil salinization has been a tremendous obstacle for agriculture production. The regulatory networks underlying salinity adaption in model plants have been extensively explored. However, limited understanding of the salt response mechanisms has hindered the planting and production in Fagopyrum tataricum, an economic and health-beneficial plant mainly distributing in southwest China. In this study, we performed physiological analysis and found that salt stress of 200 mM NaCl solution significantly affected the relative water content (RWC), electrolyte leakage (EL), malondialdehyde (MDA) content, peroxidase (POD) and superoxide dismutase (SOD) activities in tartary buckwheat seedlings. Further, we conducted transcriptome comparison between control and salt treatment to identify potential regulatory components involved in F. tataricum salt responses. A total of 53.15 million clean reads from control and salt-treated libraries were produced via an Illumina sequencing approach. Then we de novo assembled these reads into a transcriptome dataset containing 57,921 unigenes with N50 length of 1400 bp and total length of 44.5 Mb. A total of 36,688 unigenes could find matches in public databases. GO, KEGG and KOG classification suggested the enrichment of these unigenes in 56 sub-categories, 25 KOG, and 273 pathways, respectively. Comparison of the transcriptome expression patterns between control and salt treatment unveiled 455 differentially expressed genes (DEGs). Further, we found the genes encoding for protein kinases, phosphatases, heat shock proteins (HSPs), ATP-binding cassette (ABC) transporters, glutathione S-transferases (GSTs), abiotic-related transcription factors and circadian clock might be relevant to the salinity adaption of this species. Thus, this study offers an insight into salt tolerance mechanisms, and will serve as useful genetic information for tolerant elite breeding programs in future.

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future.