Comparison of nutritional compositions of foxtail millet from the different cultivation regions by UPLC‐Q‐Orbitrap HRMS based metabolomics approach

Abstract

Foxtail millet (Setaria italica (L.) P. Beauv) is cultivated throughout most parts of Shanxi province. Although quite a number of reports have been conducted on the bioactivities of foxtail millet, little information was available on the metabolite differences of the foxtail millet seeds from different cultivation regions. In this study, a systematic study on the metabolite composition of the foxtail millet seed from Shanxi province was conducted by ultra-high performance liquid chromatography combined with electrospray ionization quadrupole/orbitrap high-resolution mass spectrometry, and 158 compounds were characterized through analysis of mass fragmentation patterns and comparison with the data in the databases and literatures. Then the metabolomic analysis, in combination with heatmap and hierarchical clustering analysis revealed the significant differences between the foxtail millet from northern and southern Shanxi province, which was ascribed to 20 differential metabolites. Then metabolic pathway analysis was performed based on these differential metabolites, and three metabolic pathways were selected as the key contributors. The results showed that foxtail millet from different cultivation regions showed obvious metabolite differences, which was probably related to environmental factors. In addition, the findings also provide an important reference for further research on the functional food development from foxtail millet. Practical applicationsFoxtail millet (Setaria italica (L.) P. Beauv) as a staple food among the majority of people, is rich in bioactive nutrients, including free fatty acids, triglycerides, cellulose, protein, vitamins, and polyphenol, et al. The metabolite composition of the foxtail millet seed was investigated systematically and the results showed that the climate conditions can directly affect the metabolite composition of foxtail millets, and provided important reference for the further research on the resource utilization and functional food development from foxtail millet.

Revitalization of small millets for nutritional and food security by advanced genetics and genomics approaches

“…GWAS combined with metabolite profiling has been used to detect biochemical and genetic processes in major staples (Luo, 2015;Matsuda et al, 2015). Combined HRPF and GWAS was used to inform biomass and yield in rice (Yang et al, 2015); photosynthesis and growth rates in maize were analyzed by the combination of metabolomics and ionomics (Guo R et al, 2017); metabolomics, ionomics, and genomics involving QTL mapping were applied to evaluate iron and zinc in rice (Pinson et al, 2015); integrated mutagenomics and phenomics were used to understand increased lycopene in tomato (Li et al, 2018); mQTL analysis and mGWAS have been applied in maize and rice (Li K. et al, 2019;Tiozon, 2021), and combined Frontiers in Genetics frontiersin.org transcriptome and metabolome study have been used to understand defense response in wheat Trichoderma harzianum strain T22 (Coppola et al, 2019) to detect key metabolites and candidate genes involved in the pathways for grain yield, secondary metabolites, and disease resistance (Yang et al, 2021). Finally, functional genomics plays a major role in hybrid performance.…”

“…The integrative use of transcriptomics and metabolomics was applied to identify key pathways involved in salinity tolerance in foxtail millet ( Pan et al, 2020 ). Metabolomics analysis revealed the environmental and geographical influences on the bioactive nutrient profile of foxtail millet and the key biochemical pathways ( Yang et al, 2021 ). Thus, combining advanced techniques to understand the genetic regulation of crops provides new gateways for the development of desirable plant types.…”

“…Finally, functional genomics plays a major role in hybrid performance. The prediction of genotype performance by multi-omics is more precise than the conventional techniques ( Yang et al, 2021 ). Future goals of integrated omics include the development of a proteome and metabolome atlas depicting the major genes in metabolic pathways, which also ensures the development of advanced databases for integrated big data in molecular breeding ( Peng et al, 2020 ).…”

Development of a Comprehensive Quality Evaluation System for Foxtail Millet from Different Ecological Regions

“…We calculated the comprehensive principal component model by taking the ratio of the four principal components to the corresponding feature values and obtained the sum of the feature values of all extracted principal components as follows: The comprehensive scores were calculated using the quality evaluation system, and the results showed that foxtail millet produced in Dingxiang (0.89) had the highest comprehensive quality, followed by Zezhou (0.10), Qinxian (0.02), and Xingxian (−0.12); the quality of foxtail millet in Yuci (−0.88) was the lowest (Table 2). The quality of foxtail millet is affected by genetic traits [3], production area [22], environmental factors [21], harvest period [4,14], cultivation methods [41], and farming systems [42]. Similarly, variations in foxtail millet quality in different production areas were the result of comprehensive factors including variations in soil texture, fertility level, cultivation system, and climatic factors such as sunshine duration, temperature, and precipitation [21,29].…”

Liquid chromatography‐mass spectrometry‐based metabolomics reveals the comprehensive metabolites in Dioscorea opposita Thunb. peel

“…UHPLC-quadrupole/orbitrap HRMS (UHPLC-Q-Orbitrap HRMS) is one of the most widely employed analytical tools in metabolomics studies [16], because of the efficient separation capabilities of UHPLC and excellent structural elucidation by HRMS [17]. UHPLC-Q-Orbitrap HRMS-based metabolomics studies can identify common constituents and valuable active ingredients [18,19], making it a powerful method to obtain comprehensive information on both known and unknown metabolites in the metabolome [20].…”