Annotation of genes involved in gamma-aminobutyric multiplication during coix germination by metabolome and transcriptome analysis

ABSTRACT

Job’s tears (coix) is a widely used grain with rich and balanced nutritional value. In this study, a total of 71 metabolites, including amino acid metabolism, sugar metabolism and fatty acid metabolism, were significantly increased in the germinating coix through liquid chromatography tandem mass spectrometry (HPLC-MS/MS). Significant increase in gamma-aminobutyric (GABA) content during seed germination was detected in the metabolites detected. Transcript profiles generated through high-throughput RNA-sequencing data and 2431 differentially expressed genes (DEGs) were identified. The functional of DEGs were also significantly clustered in glucose metabolism and fatty acid metabolism. In the annotation of metabolite variation and differentially expressed genes, 23 regulatory genes and 10 significantly enriched metabolites for GABA content proliferation during coix germination were identified by association analysis. These results provided a theoretical basis for understanding the germination mechanism of coix and promoting its production and application.

KEYWORDS:

• Job’s-tears (coix)
• gamma-aminobutyric
• metabolome
• transcriptome
• seed germination

Introduction

Seed germination is an important biological process, the seeds underwent a series of physiological and biochemical changes, such as starch and protein hydrolysis, macromolecule synthesis, enhanced respiration, subcellular structural changes and cell elongation, etc. (Carrera-Castaño et al., 2020; Colmer et al., 2020). Since seeds cannot obtain energy from the outside during the germination stage, they must rely on the decomposition of their own stored materials to provide the necessary energy for growth and development (Ding et al., 2018; Liu et al., 2018; Nietzel et al., 2020).

Sprout food generally refers to sprout vegetables, such as buds, shoots buds, young shoots or young stems, which can be cultivated under certain conditions with plant grains or their vegetative bodies (Geng et al., 2022). In addition to being rich in various nutrients required, bud foods also contain dietary fiber, which can meet the human normal needs, ensure the smooth digestive tract and promote human health (Zhang et al., 2019a). During the germination process of plant seeds, the activated endogenous phytase can effectively degrade phytic acid and provide mineral and inorganic elements for the metabolism of seedlings (Bareke, 2018). Under the action of protease, the storage proteins are hydrolyzed into free amino acids and further converted into soluble nitrogen compounds and energy, which make the germinated seeds have high nutrition and some special health care functions (Gravitz, 2012). At the same time, proteins, starch and lipids are degraded by related enzymes into polypeptides, oligosaccharides and unsaturated fatty acids and synthesize vitamins, gamma-aminobutyric acid and other active substances beneficial to human health. Antinutritional factors such as trypsin inhibitor can also be degraded, which greatly improves the nutritional value and flavor of grains (Damaris et al., 2019; Gomes et al., 2019).

Gamma-aminobutyric acid (GABA), with molecular formula C₄H₉NO₂, is a naturally occurring non-protein amino acid, which widely exists in plants and animals. In plants, GABA is widely found in seeds, roots and tissue fluids, such as cereals, beans, vegetables, fruits and nuts, and other plants. In animals, GABA is mainly expressed in their nervous tissues and is considered to be an important inhibitory neurotransmitter in the central nervous system (Kinnersley & Turano, 2000; Luo et al., 2021). About 30% of the synaptic sites in the central nervous system use GABA as a transmitter, it can be healthy brain puzzle, fall blood pressure, activation liver function, promote growth hormone secretion, known as “the natural sedative of the brain” (Fischer et al., 2019; Hampe et al., 2018). The accumulation of GABA will become difficult with the increase of age and mental stress, so the use of daily dietary supplements can effectively improve this situation, which is beneficial to human health (Maiti et al., 2017). Due to the low content of GABA in plant materials, it is difficult to extract large amounts of GABA from these natural tissues (Dhakal et al., 2012; Yu et al., 2019), the preparation methods of GABA mainly include chemical synthesis and biosynthesis (Nikmaram et al., 2017; Ramos-Ruiz et al., 2018). Chemical synthesis method conditions are severe, and there are chemical residues, not suitable for food application (Dhakal et al., 2012). The biosynthesis method mainly uses GAD (mainly from microorganisms and plants) in biomaterials to convert Glu to produce GABA. When developing GABA food, using E. coli directly will have potential safety risks (Rashmi et al., 2018; Yu et al., 2019). The plant enrichment method generates GABA through the stress response of plants under stress conditions, including hypoxia, low temperature, drought, high salt, high H⁺ concentration, mechanical damage and insect pest, etc (Akama et al., 2009; Rashmi et al., 2018; Vann et al., 2020). When brown rice germinates, endogenous enzymes are activated and GABA is produced, which is a safe and healthy process of biosynthesis of GABA (Cho & Lim, 2016; Hussain et al., 2020; Park & Oh, 2005). In recent years, germinated brown rice has appeared successively in some rice-consuming areas, and some related germinated brown rice products appear on the market, but their form is relatively simple. At present, germinated brown rice and its processed products have become one of the research hotspots (Cho & Lim, 2016; Patil & Khan, 2011; Tian et al., 2004).

Coix lacryma-jobi L. (Job's tears), also known ascoix, is an annual or perennial C4 gramineae, which is widely cultivated in China, India, Vietnam, Myanmar, Thailand, Philippines, Malaysia, Japan and other countries (Song et al., 2015). Coix is a kind of nutritious and balanced grain, and its protein content is much higher than wheat and rice, contains many essential amino acids and trace elements (Ding et al., 2021). Non-saturated fatty acid content is higher in the seeds, especially oleic acid and linoleic acid, up to 75% (Ding et al., 2021). The byproducts of coix bran, bran layer and husk are good oil sources and have good economic value and development prospect (Feng et al., 2020). Most importantly, as a medicinal and edible plant, coix is known as “the king of grass plants” and “pearl of medicine,” because of its high medicinal and nutritional value (Devaraj et al., 2020). The fruits, roots and leaves of coix can be used as medicine, and have medicinal values in immune regulation, antiviral, blood pressure reduction, blood glucose reduction and anti-tumor (Jinnouchi et al., 2021; Suzuki & Konaya, 2021). In recent years, with the popularization and promotion of healthy life concept of advocating health and returning to nature, the health care function and development and utilization of coix have been gradually put on the agenda. The current researches mainly focus on the regional distribution, genetic diversity, evaluation and utilization of germplasm resources, planting density and cultivation techniques of coix (Huang et al., 2020; Zhang et al., 2019b). But it is rarely reported about the accumulation and variation of metabolites in seed germination of coix.

In this study, comprehensive analysis of transcriptome and metabolome profiles alterations during seeds germination in coix, which has theoretical significance and practical application value to explore the changes of nutrient composition and accumulation of active substances in coixduring seeds germination.

Methodology

Plant materials

The healthy and plump coix seeds of the same size were selected, disinfected with 1% sodium hypochlorite, washed with distilled water for 3–4 times, and then placed in a petri dish (9 cm in diameter) covered with two layers of filter paper, with 30 seeds in each dish. The seeds treated above were placed in an incubator at 25°C for cultivation. 72 h later, the germinated seeds (the number of white seeds was recorded based on the breakthrough of radicle through seed coat) were sampled for transcriptome and metabonomics analysis, and treated seeds were used as controls.

Metabolite extraction and HPLC-MS/MS analysis

A total of 6 biological replicates were set up for metabolic analysis, and 30 coix seeds were selected in each replicate. All chemicals and solvents were analytical or HPLC grade. High-performance liquid chromatography grade methanol, water, formic acid, acetonitrile, and ammonium bicarbonate were purchased from CNW Technologies GmbH (Düsseldorf, Germany). L-2-chlorophenylalanine was from Shanghai Hengchuang Bio-technology Co., Ltd. (Shanghai, China).

80 mg accurately weighed sample was transferred to a 1.5 mL Eppendorf tube. Two small steel balls were added to the tube. 20 μL internal standard in methanol and water (1/1, vol/vol) and 1 mL mixture of methanol and water (7/3, vol/vol) were added to each sample, samples were placed at −80°C for 2 min. Then grinded at 60 HZ for 2 min, and ultrasonicated at ambient temperature for 30 min after vortexed, then placed at 4°C for 10 min. Samples were centrifuged at 14,000 rpm, 4°C for 10 min. The supernatants (500 μL) from each tube were collected using crystal syringes, filtered through 0.22 μm microfilters and transferred to LC vials. The vials were stored at −80°C until LC-MS analysis (Smith et al., 2006). QC samples were prepared by mixing aliquots of the all samples to be a pooled sample. Carnitine C2:0-d3, Carnitine C8:0-d3, Carnitine C10:0-d3, Carnitine C16:0-d3, LPC 19:0, FFA C16:0-d3, FFA C18:0-d3, CDCA-d4, CA-d4, Trp-d5, Phe-d5, SM12:0 and Choline-d4 were added to QCs for retention time calibration. The QCs were injected at regular intervals (every eight samples) throughout the analytical run to provide a set of data from which repeatability can be assessed.

ACQUITY UHPLC system Ultimate 3000 (Thermo Fisher Scientific, Waltham, MA, USA) coupled with LTQ Orbitrap MS (Thermo Fisher Scientific, Waltham, MA, USA) was used to analyze the metabolic profiling in both ESI positive and ESI negative ion modes. In positive ion mode, the separation of metabolites was conducted on a 2.1 × 100 mm ACQUITYTM 1.7 μm BEH C8 column, and the mobile phase contained water with 0.1% formic acid (A) and acetonitrile (B). The linear elution gradient program was used as follows: 5% B kept 1.0 min, then linearly increased to 100% B at 24 min, and held for 4 min, 100–5% from 28 to 28.1 min, and held at 5% from 28.1 to 30 min. Each run time was 30 min. In negative ion mode, the metabolite separation was performed on 2.1 × 100 mm ACQUITYTM 1.8 μm HSS T3 column, and the mobile phase contained 6.5 mM ammonium bicarbonate water solution (C) and 6.5 mM ammonium bicarbonate in 95% methanol and water (D). The linear elution gradient program was 5% D kept 1.0 min, then linearly increased to 100% D at 18 min, and held for 4 min, 100–5% from 22 to 22.1 min, and held at 5% from 22.1 to 25 min. Each run time was 25 min. The flow rate was 0.35 mL/min and column temperature was 50°C. The injection volume was 5 μL (Dunn et al., 2011; Haspel et al., 2014; Yuan et al., 2012). Mass spectrometry detections were set as the following: capillary temperature 350 and 360°C, spray voltage 3.5 and 3.0 kV for positive ion mode and negative ion mode, respectively. The mass scan range was m/z 50–1000. The resolution of the MS was set to 30,000 (Jia et al., 2016; Lin et al., 2014).

RNA extraction and transcriptome analysis

RNA was extracted and tested for its concentration, purity and integrity (Du et al., 2019). After qualified samples were detected, the mRNA was fragmented into short fragments by fragmentation buffer, and PCR enrichment was performed to obtain the final cDNA library. After qualified library inspection, HiSeq (Illumina) sequencing is performed after pooling of different libraries according to the requirements of effective concentration and target offline data amount. DESeq2 R language package was used to analyze the expression of differential genes in the two comparison combinations and the benjamini-Hochberg method was used to adjust the p values to control the false discovery rate. The module CuffDiff uses Cufflinks based on Reads Per Kilobase of exon model per Million mapped reads (FPKM) (Anders et al., 2015; Anders & Huber, 2012; Trapnell et al., 2010). p < 0.05 and fold-change > 1 were set to determine the differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/kegg/ko.html) were analysis using DEGs (Kanehisa et al., 2007).

Results

Seed germination and metabolic analysis through HPLC-MS/MS

In this study, after 72 h of germination, significant roots and buds were observed from the seeds of coix, forming a complete plant. At this time, the buds did not begin to unfold and could not undergo photosynthesis (Figure 1(a)). HPLC-MS/MS method was used to detect the metabolic components of seeds at 72 h after germination (Figure 1(b)), compared with the no germinated seeds as control. Typical ion chromatograms (TIC) of seed metabolomics showed different varieties at different developmental stages (Figure 1(c)). After seed of oix in different states was tested by machine, the characteristic mass spectrum peaks of metabolites detected by each multiple reaction monitoring mode (MRM) were overlapped, and the multi-peak diagram of all metabolites detected in this experiment was obtained.

Figure 1. Seed germination and metabolic analysis through HPLC-MS/MS. (a) Phenotype of the un-germination seed (CK) and the germination seed (DX). (b) Typical ion chromatograms (TIC) of seed metabolomics at different developmental stages. The total ion flow chromatography is a continuous scanning of the component of the separation of the flow of the chromatographic spectrum, and the mass spectrometry is collected by the continuous scanning of the mass spectrum, and each scan is obtained by a mass spectrograph, which is added to all the ions in each mass spectrum, and obtains a total ionic flow intensity. And then the ion intensity is the column coordinate, the time is the horizontal coordinate, the draw is made. (c) Principal Component Score (PCA) analysis of metabolome sequencing results. The X-axis represents the first principle component (PC1), and the Y-axis represents the second principal component (PC2). PC1 explains 76.2% variance distinguishing genotypes from different time points, and PC2 explains 9.53% variance is distinguishing genotypes based on biological replications. (d) Orthogonal projections to latent structures- discriminant analysis (OPLS-DA) results and S-plot score. The X-axis represents the first principle component (PC1), and the Y-axis represents the second principal component (PC2). PC1 explains 72% variance distinguishing genotypes from different time points, and PC2 explains 9.45% variance is distinguishing genotypes based on biological replications.

Principal component analysis (PCA) of metabolomics data of coix by HPLC-MS/MS detection showed that R2X [1] and R2X [2] were 0.762 and 0.0953, respectively (Figure 1(d)). However, the metabolic components of coix seed groups were relatively scattered before and after germination, indicating that there were great differences in metabolic components of coix seed groups that began to germinate compared with non-germinated seeds. In summary, the duplicate samples were clustered together, there were no significant outliers, and the difference groups were clearly separated. In order to further compare the differences between CK and DX samples, OPLS-DA with displacement test was used for analysis. The results show that CK group and DX group samples with different treatments can be distinguished well. In the Figure 1(e), metabolomics data of showed that R2X [1] and R2X [2] were 0.72 and 0.0945, respectively, showed that there were differences between the two groups before and after seed germination but the metabolic profiles were very similar.

Statistical description of metabolic analysis

In this study, a total of 599 metabolites in coix seeds of different states were detected based on widely targeted metabolome technology. The annotated metabolites were classified according to their chemical structures. All the metabolites were classified into eight super categories, organic acid (31%), amino acid (30%), phosphoric acid (10%), poly ol (7%), sugar (10%), fatty acid (3%), amine (1%), and others (8%). More detailed metabolite class is shown in Figure 2, it covers monomethylphosphate (27), phosphoric acid (24), 2,4-Dihydroxybutanoic acid (33), malic acid (29), citric acid (34), fructose (6), glucose (24), mannitol (32), alanine (32), leucine (33), valine (35), 3-hydroxy-Pyridine (28), 2-Aminobutyric acid (25), isoleucine (33), proline (37), glycine (26), serine (34), threonine (34), beta-Alanine (12), methionine (32), aspartic acid (32), phenylalanine (32), cis-Aconitic acid (32), glycerol-3-phosphate (35), glutamine (34), quinic acid (33), lysine (36), tyrosine (33), fructose-6-phosphate (37), glucose-6-phosphate (35), parabanic acid (35), mannose-6-phosphate (34), pyroglutamic acid (33), myo-Inositol-1-phosphate (34), 2-Ketoglutaric acid (21), isomaltose (30), glutamic acid (8), maltose (6), ethanolamine (5), 4-Aminobutyric acid (7), asparagine (16), glyceric acid (5), 1-Monohexadecanoylglycerol (15), adenosine (1), trehalose (2), pyruvic acid (8), lactic acid (26), urea (1) and 2,3-Dihydroxybutanedioic acid (2). Relative quantitative analysis of metabolic components was performed on seed metabolomics data obtained by HPLC-MS/MS detection before and after seed germination. The results showed that the content of 70 metabolites showed an upward trend except that urea content significantly decreased. The types of substances where elevated levels were detected included amine (1), organic acid (22), amino acid (21), phosphoric acid (7), polyol (5), sugar (7), fatty acid (2) and others (6).

Figure 2. Statistical description of metabolic analysis of coix seed germination.

Screen of differentially accumulated metabolites (DAMs)

Through the screening of metabolites, 49 metabolites with different changes were shown in Figure 3(a), among which 48 were significantly up-regulated and enriched after seed germination, except for the significantly down-regulated and enriched urea content. According to KEGG, metabolites were divided into 48 categories, mainly including aminoacyl-tRNA biosynthesis, purine metabolism, fatty acid biosynthesis, pyrimidine metabolism, arginine and proline metabolism, amino sugar and nucleotide sugar metabolism, cysteine and methionine metabolism, valine, leucine and isoleucine degradation, porphyrin and chlorophyll metabolism, phenylpropanoid biosynthesis, glycine, serine and threonine metabolism, valine, leucine and isoleucine biosynthesis, galactose metabolism, glutathione metabolism, starch and sucrose metabolism, glycolysis or gluconeogenesis, glycerophospholipid metabolism, tryptophan metabolism, terpenoid backbone biosynthesis and phenylalanine, tyrosine and tryptophan biosynthesis. The enriched differential metabolites were significantly correlated with the metabolites detected in some existing seed germination studies, and the significant enrichment of 0.059341 (Raw p) was also detected in the aminobutyric acid metabolism process concerned in this study.

Figure 3. Untargeted metabolome profiling after the seed germination. (a) The unsupervised hierarchical cluster analysis for untargeted metabolomic profiles. The dendrogram was built based on the fold change (p < 0.05, VIP > 1, and log2FC > 1.50) heat map. The rows in the heat map indicate metabolites, and the columns represent groups/samples. The colors of heat map cells represent the scaled expression level of metabolites among different groups. The color gradient, ranging from green to black through red, indicates the low, high, and middle values of metabolite expression. Colors of different groups represent the biological replicates at two different time points. The green dot in the figure represents the down regulated differential expression metabolite, the red dot represents the up regulated differential expression metabolite, and the black dot represents the detected but insignificant metabolite. (b) Metabolite association analysis of coix seed germination. Each circle represents various metabolites, and the line between them represents the significant correlation between metabolites and metabolites, among which red represents positive correlation, blue represents negative correlation.

The metabolomics data of different coix varieties obtained by HPLC-MS/MS detection and seed at different filling stages were analyzed by association network, namely, the correlation analysis between metabolites and metabolites. As shown in Figure 4(b), each circle represents a variety of metabolites, and the lines between them represent significant correlations between metabolites and metabolites. Network analysis, using the detected differentially expressed metabolites, the KEGG enriched by differentially expressed genes was divided into five parts, including 36, 9, 2, 1 and 1 network respectively. Among the significantly related metabolites, there are pyruvic acid, lactic acid, alanine, 3-hydroxy-pyridine, 2-aminobutyric acid, monomethylphosphate, valine, ethanolamine, leucine, phosphoric acid, isoleucine, proline, glycine, serine, threonine, 2,4-dihydroxybutanone ic acid, beta-Alanine, malic acid, parabanic acid, methionine, pyroglutamic acid, aspartic acid, 4-Aminobutyric acid, 2,3-Dihydroxybutanedioic acid, glutamic acid, phenylalanine, asparagine, cis-Aconitic acid, glycerol-3-phosphate, glucose, lysine, mannitol, tyrosine, fructose-6-phosphate, mannose-6-phosphate and urea. Furthermore, the established network was significantly enriched in the amino acid metabolism pathway, which was highly consistent with the previous studies on seed germination, indicating that there were abundant variations in amino acid metabolism, including butyric acid metabolism, during seed germination.

Figure 4. Gene expression analysis during the seed germination, generated using high-throughput RNA-seq. (a) Venn diagram for genes between germination seed (C2) and control (C1). (b) Identification of differentially expressed genes (DEGs) between treatments, generated using high-throughput deep sequencing technology. The volcano plot presents the expression of the DEGs in different treatments, the red dots represent up-regulated genes, and the green dots represent down-regulated genes. (c) The KEGG pathways of all DEGs of germination seeds and controls. The abscissa is the percent of differentially expressed genes in different KEGG, and the ordinate is the type of KEGG enrichment.

Differential expression gene analyzed by high-throughput transcriptome

Expression genes were identified by transcriptome sequencing during the two germination stages, a total of 67,257 genes were identified with the FPKM > 0.1. The detected genes were shown by Venn diagram (Figure 4(a)). Among the detected genes, 47,170 genes were detected in both periods, 5157 genes were detected only in the non-germination stage, and 14,930 genes were specifically expressed after germination. A total of 1246 DEGs with significant variation were identified between C1 and C2 (Figure 4(b)), among which 1181 genes were up-regulated and 65 genes were down-regulated. The variation trends of gene expression number and expression amount indicated that a large number of genes were initiated in the early seed germination, and with the increase of gene expression level, various metabolic activities of seeds were driven, and seeds of coix entered a state of enhanced life activities. The differentially expressed genes identified at two stages of seed germination were compared with the GO (Gene Ontology) database to obtain detailed annotations of differentially expressed genes. Results showed that 2431 differentially expressed genes were annotated in the GO database, and almost all of them were up-regulated genes. Significantly aggregated GO included macromolecule localization, ATP metabolic process and membrane protein complex, and genes were enriched by KEGG analysis (Figure 4(c)).

Metabolic regulation of GABA through metabolome and transcriptome analysis

Comprehensive analysis of transcriptomic and metabolomic data showed that amino acid metabolism, carbohydrate metabolism and lipid metabolism significantly changed during seed germination, thus initiating the biological processes of coix. The 7 pathways with the largest number of genes enriched to differentially expressed genes were Translation, Carbohydrate metabolism, Folding, sorting and degradation, Overview, animo acid metabolism, transport and catabolism and lipid metabolism. The existing research data show that the important physiological function of GABA in plants is to participate in signal transduction and alleviate stress. The metabolic pathways related to GABA metabolism are mainly citrate cycle (TCA cycle), butanoate metabolism and alanine, aspartate and glutamate metabolism. The citrate cycle (TCA cycle) is mainly the intermediate product required for GABA metabolism. Butanoate metabolism and alanine, aspartate and glutamate metabolism are directly related to GABA synthesis. In this study, 8 metabolites were detected to be related to citrate cycle (TCA cycle). These metabolites showed significant enrichment during germination (Figure 5(a–h)). Through transcriptome analysis, 9 genes were detected to be differentially expressed and related to this metabolic process (Figure 5(k)). Among the nine genomes, the expression levels of five genes JG13731, JG14330, JG14330, JG16355 and JG9239 were down regulated, and the other four genes (JG10475, JG12470, JG13760 and JG7401) were up-regulated after seed germination. Among them, JG16355 was significantly down-regulated and JG10475 was significantly up-regulated expression. The three metabolites were related to Butanoate metabolism and showed significant enrichment (Figure 5(e–g)). After seed germination, six genes were detected to be differentially expressed (Figure 5(k)), of which three genes were up-regulated (JG12394, JG12939 and JG14018) and three genes were down regulated (JG10157, JG12600 and JG8513). Eight differentially expressed genes were detected by transcriptome analysis (5 down-regulated and 3 up-regulated), resulting in significant enrichment of 2-Ketoglutaric acid (Figure 5(h)), cis-Aconitic acid (Figure 5(i)), Citric acid (Figure 5(j)) and Pyruvic acid (Figure 5(c)) after seed germination.

Figure 5. Differential metabolites and differentially expressed genes related to GABA synthesis pathway. (a–j) In the box plot, the abscissa represents the sample and the ordinate represents the enrichment content for the differentially enriched metabolites. Blue is the test result of non-germinated seeds, and red is the test result of germinated seeds. (k) The column chart is about differentially expressed genes. The abscissa represents the gene name and the ordinate represents the expression amount. Blue is the test result of non-germinated seeds, and red is the test result of germinated seeds.

Discussion

Gamma-aminobutyric acid (GABA) is a naturally occurring non-protein amino acid, also as an important inhibitory neurotransmitter in the central nervous system (Gramazio et al., 2020; Hou et al., 2022; Oh et al., 2003), participates in various metabolic and physiological activities in human body (Hampe et al., 2018; Kinnersley & Turano, 2000; Luo et al., 2021). GABA has become an important research topic and development idea for domestic and foreign beverage manufacturers in the development of new products, increase the added value of products, strengthen the functionality of products, and has a broad market prospect. At present, the main methods of GABA preparation include chemical synthesis (Guetzoyan et al., 2013), biosynthesis based on fermentation engineering (Schousboe et al., 2013; Takagi et al., 2022) and enrichment of germinated brown rice (Komatsuzaki et al., 2007; Zhang et al., 2019c).

Sprout food with its special health care function in the food industry alone tree sail. By adopting biotransformation technology to control the germination process of grain seeds, it can not only transform the nutrients in the seeds, but also enrich the substances that have physiological activity to human body, and improve the nutritional value and health care function of seeds. In recent years, sprout foods with natural health functions have emerged quietly, and the products in the market are of different forms, and relevant studies at home and abroad have been reported frequently (Thomas et al., 2003). China has a long history of sprout food production, bean sprouts, malt, alfalfa and other favorite foods of Chinese people since ancient times. For example, the increase of isoflavone content in sprouted soybean and the enrichment of GABA in sprouted brown rice enhance the health care function of the products (Cho et al., 2008; Kim & Kim, 2020; Wang et al., 2022). Barley treated with sodium selenite enriched with organic selenium after impregnation and germination (Islam et al., 2019; Rico et al., 2020).

In this study, the changes of gene expression and metabolites in coix seed at 72 h were analyzed by high-throughput transcriptome and metabolome analysis. A total of 1246 significantly different DEGs were identified, and most of them showed a trend of up-regulated expression, which was consistent with the trend of enhanced biological activity during seed germination. For the analysis of metabolites, a total of 71 kinds of metabolites with variable content were detected. Except urea content decreased in germinated seeds, the contents of 70 metabolites were significantly up-regulated, which was consistent with the changes of gene expression, indicating that seed germination was a process of enhanced biological activity. These phenomena have also been found and elaborated in related studies, demonstrating that the physicochemical changes and expression regulation of seed germination are similar in different species (Song et al., 2015). At present, studies on coix germinated have just started. It has theoretical significance and practical application value to explore the changes of nutrient composition and accumulation of active substances in Job’s tears during germination. It is not difficult to see that coix germinating can significantly improve its nutritional value, increase the accumulation of reducing sugars, amino acids and other substances, reduce the effect of anti-nutritional factors, improve the availability of mineral elements and protein digestion and absorption rate. In particular, germinated coix can significantly enrich GABA, which can be used as a potential material for the preparation of plant GABA and the development of functional food based on coix germ.

Conclusions

Coix is a kind of crop with edible and medicinal values, the analysis of germination process is helpful to understand and apply. In this study, transcriptome and metabolome analysis were used to analyze the germination of coix, confirmed that a series of key genes and important metabolites are related to GABA synthesis.

Authors contributions

DH conceived and designed the experiments. DH and DD wrote the manuscript. YH and GD performed the experiments. DD and YH analyzed the data. XZ and YD revised the manuscript. DH and DD contributed equally to the paper. All authors have read and gave final approval for publication. The authors declare no conflict of interest.

Ethics approval and consent to participate

The samples used in this study were grow in China and collected by our lab (College of Biological Sciences and Technology, Taiyuan Normal University (Taiyuan, Shanxi, China). Special thanks to Senior Agronomist Xiangdong Li of Karst Regional Development Institute, Southwest Guizhou for his outstanding contribution in the early collection of germplasm resources! All authors stated that this study comply with the Chinese legislation and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Availability of data and materials

The datasets supporting the conclusions of this article are included in the article and its additional files. The dataset and materials presented in the investigation is not publicly available due to privacy concern but will be available from the corresponding author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by The Key Laboratory of Plant Resources Conservation and Germplasm Innovation in Mountainous Region (Ministry of Education-2019-034), the Critical Talent Workstation Project (TYSGJ202201), the Basic Research Program of Shanxi Province (202203021222242), the National Key R&D Program of China (2019YFD1001300, 2019YFD1001303), the Natural Science Foundation of China (31960415), and the Teaching Content and Curriculum System Reform Project of Higher Education Institutions in Guizhou Province (2019202, 2020035), the Scientific and Technological Innovation Project of University in Shanxi Province (grant number2019L0802).