Growth, ROS Markers, Antioxidant Enzymes, Osmotic Regulators and Metabolic Changes in Tartary Buckwheat Subjected to Short Drought

1  Introduction

Drought is the most important environmental constraint to crop productivity and food security worldwide [1]. Water deficit has pronounced effects on plant growth, survival, physiology, and yield [2,3]. Plants subjected to drought undergo evident morphological modifications, including reduced shoot growth (leaves and stems), and changes in root diameter, length, branches and lateral root formation, all of which disrupt plant–water relations and affect water-use efficiency [4,5]. Drought interferes with many physiological processes, including CO2 assimilation and metabolism, ultimately leading to a significant reduction in biomass accumulation [6,7]. However, plants respond to drought through physiological and metabolic changes, such as inhibited transpiration through a reduction in leaf area or stomatal closure, to successfully minimize water loss [8]. Drought induces the production of reactive oxygen species (ROS) and modifies antioxidant systems [5,9]. Plants can improve their capacity for osmotic adjustment by accumulating solutes to maintain cell turgidity, protecting against cellular oxidative damage, and altering metabolic pathways to tolerate or resist water deficit [10,11]. Furthermore, the plant cell undergoes changes at the transcript, protein, and metabolite levels to acclimate to water deficit [11–14].

Metabolomics, also known as metabonomics or metabolic profiling, provides a useful and rapid method for assessing the changes occurring in the metabolome as a consequence of the environment [1,15–17]. It is a powerful tool for analyzing plant responses to environmental stimuli, by identifying changes in the profile of primary and secondary metabolites [15,18–20]. Primary metabolism, including carbon and nitrogen metabolism, is altered by drought. Primary carbon metabolites such as polyols and sugars, and nitrogen-storage metabolites such as quaternary ammonium compounds or amino acids, have been found to increase in response to drought, mainly in plant roots [20]. Secondary metabolites, produced by pathways derived from primary metabolic routes, also play an important role in the drought response [17,21]. Water stress has been reported to lead to increases in gamma-aminobutyric acid, trigonelline, cyanogenic glycosides, glucosinolates, terpenoids, alkaloids and condensed tannins [2,16]. Under drought stress, the flavonoid pathway regulates the production of reactive oxygen species (ROS) [21]. Of the root-specific metabolites, gamma-glutamyl compounds have been found to accumulate significantly in response to drought stress reflecting induction of the glutathione pathway [21]. Plant hormones, such as gibberellic acid, indoleacetic acid (auxin), ethylene, cytokinin, abscisic acid and brassinosteroids, as well as signaling compounds involved in hormone metabolism, have been widely studied for their involvement in the drought response [22–26]. ROS and reactive nitrogen species metabolism is active in drought-stressed roots, characterized by increased protein nitration and accumulation of nitric oxide and S-nitrosothiols in cortical cells [3]. In addition to elucidating the metabolic differences between developmental stages or organs, it is essential to achieve an integrated understanding of the whole-plant response [21].

Tartary buckwheat (Fagopyrum tataricum) is a traditional short-season pseudocereal crop in the family Polygonaceae [27] that contains diverse health-promoting compounds, such as flavonoids, anthocyanins, phenolics, amino acids and vitamins [28–30]. Tartary buckwheat is widely distributed in Asia, in the marginal lands of mountainous areas of Western China, the Himalayas, Japan and Korea, as well as in Europe and the Americas [27,31]. Tartary buckwheat grows in environments that are harsh and cold, with strong ultraviolet radiation and frequent and severe drought conditions in high-altitude mountainous areas, and in arid or semiarid regions, causing defective growth and yield loss of Tartary buckwheat. However, a lack of metabolic profiling seriously limits our understanding of the drought-responsive mechanisms in Tartary buckwheat. An elucidation of these mechanisms should indicate specific traits to be targeted in selection programs, and help in implementing crop-management practices under drought conditions. In this study, we provide insight into the mechanism of Tartary buckwheat responses to drought in terms of plant growth and metabolome profile.

2  Materials and Methods

2.1 Plant Materials and Site Description

F. tataricum variety Chuan Qiao No. 1(CQ1), one of the main varieties in China which is widely used for field production, was used in this experiment. The seeds were cultured in a special device with nutritional supply and then placed in a growth chamber (25°C). The special device was designed for seed germination and growth under drought stress, without destroying the plant and allowing for seedling emergence [32]. For the water-deficit treatment, the 10-d-old Tartary buckwheat seedlings were treated with 25% (w/v) polyethylene glycol (PEG-6000) solution (Sigma-Aldrich, USA) for 3 days. Control plants were well-watered. Samples were collected from the aerial part of the seedlings in three independent biological replicates on days 1, 2 and 3 of the treatment for physiological index, while another six independent biological replicates were collected on day 2 of the treatment and immediately frozen in liquid nitrogen for metabolic profiling.

2.2 Sampling and Measurement Methods

2.2.1 Seedlings Growth, Biomass and Relative Water Content (RWC)

Stem lengths and main root lengths of 10 plants were recorded on day 3 of the treatment to calculate the ratio of root to stem length. Plants were oven-dried at 65°C until a constant weight was obtained. The average dry weight of 10 plants was calculated and expressed on a per plant basis (± SE). RWC was determined from fresh weight (FW), dry weight (DW), and turgid weight (TW) using the formula RWC (%) = [(FW-DW)/(TW-DW)] × 100 [33].

2.2.2 ROS Markers

Electrolyte leakage was measured by the method as described in a previous study [34]. The content of malondialdehyde (MDA) was measured using the thiobarbituric acid colorimetric reaction (TBARS) assay [35].

2.2.3 Extraction and Analysis of Activity of Antioxidant Enzymes

Superoxide dismutase (SOD) activity was measured as described in a previous study [36]. Peroxidase (POD) and catalase (CAT) activities were determined according to a previous study [37], and amylase activity was measured as described before [38].

2.2.4 Analysis of Osmotic Regulators

Soluble protein content was measured by Bradford protein assay, soluble sugar content was measured as described before [39], and proline (Pro) content was quantified spectrophotometrically [38].

2.2.5 Gas Chromatography–Mass Spectrophotometry (GC–MS) Analysis

A plant sample (100 mg) was placed in a 5 mL centrifuge tube. Five steel balls were placed in liquid nitrogen for 5 min. The tubes were placed in a highflux organization grinding apparatus set at 70 Hz for 1 min. Methanol (1000 μL precooled at −20°C) was added and the mixture was vortexed for 30 s. The tubes were then placed into an ultrasound machine at room temperature for 30 min. Later, 750 μL chloroform (precooled at −20°C) and 800 μL deionized water (4°C) were added to the tubes. Then the tubes were vortexed for 60 s, centrifuged for 10 min at 12 000 rpm (GIVE IN g) at 4°C. 1 mL of the supernatant was transferred to a new centrifuge tube. Samples were vacuum-concentrated and then dissolved in 250 μL aqueous methanol solution (1:1, 4°C), filtered through a 0.22-μm membrane, and submitted for LC–MS analysis. As a quality control, 20 μL was taken from each prepared sample extract and mixed (quality control samples were used to monitor deviations of the analytical results from these pooled mixtures and compare them to the errors caused by the analytical instrument itself). The rest of the sample was used for LC–MS detection.

Chromatographic separation was accomplished in an ACQUITY UPLC system equipped with an ACQUITY UPLC® HSS T3 (150 × 2.1 mm, 1.8 μm, Waters) column maintained at 40°C. The temperature of the autosampler was 4°C. Gradient elution of the analytes was carried out with 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B) at a flow rate of 0.25 mL min−1. A 4-μL aliquot of the sample was injected after equilibration. An increasing linear gradient of solvent B (v/v) was used as follows: 0–1 min, 2% B; 1–9.5 min, 2%–50% B; 9.5–14 min, 50%–98% B; 14–15 min, 98% B; 15–15.5 min, 98%–2% B; 15.5–17 min, 2% B.

The HPLC-ESI-MSn experiments were executed in a Thermo LTQ–Orbitrap XL mass spectrometer with capillary voltages of 4.8 and 4.5 kV in negative (ESI−) and positive (ESI+) modes, respectively. Sheath gas and auxiliary gas were set at 45 and 15 arbitrary units, respectively. The capillary temperature was 325°C. The voltages of the capillary and tube were 35 and 50 V, and −15 and −50 V in positive and negative modes, respectively. The Orbitrap analyzer scanned over a mass range of m/z 89–1000 for full scan at a mass resolution of 60 000. Data-dependent acquisition in the MS/MS experiments was performed with collision-induced dissociation scan. The normalized collision energy was 30 eV. Dynamic exclusion was implemented with a repeat count of 2, and exclusion duration of 15 s.

2.3 Data Pretreatment and Multivariate Statistical Analysis

Data processing and analysis were performed according to Peng et al. [40] with slight modifications. The acquired MS data were analyzed by ChromaTOF software (v. 4.34, LECO, St. Joseph, MI). The dataset was normalized using the summed intensity of the peaks in each sample. In total, 546 peaks were detected in the tissues by GC–MS. The SIMCA-P + 13.0 software package (Umetrics, Umeå, Sweden) was used to analyze the GC–MS data. To visualize metabolic alterations in the experimental group, both unsupervised (principal component analysis, PCA) and supervised (orthogonal partial least squares discriminant analysis, OPLS-DA) segregation was exported. The parameters R2X(cum), R2Y(cum) and Q2(cum) were calculated to assess the quality of the models. After multivariate analysis, significant differences in each group were defined as having variable importance in projection (VIP) > 1.2, significance at P < 0.05 (t test) and fold change > 2; significantly altered metabolites were selected as candidates for further analysis.

2.4 Screening and Identification of Metabolic Differences

Differentially expressed compounds in the treatment group were selected by comparison to their control values using multivariate statistical method. Metabolites with both multivariate and univariate statistical significance (VIP > 1.0 and P < 0.05) were annotated with the aid of available reference standards in our laboratory, the NIST 05 standard mass spectral databases and the Feinh databases linked to ChromaTOF software. A similarity of > 70% was considered the reference standard. Based on the results of the enrichment, a customized vertical version of the histogram was created according to the significant vertical version of the top 10 entries in the column.

Statistical analyses were conducted using Excel 2010, SPSS 13.0 (Chicago, IL, USA) and SigmaPlot 10.0 (Aspire Software Intl., Ashburn, VA, USA). One-way ANOVA was used to determine the significance level, and means were compared by least significance difference (LSD) multiple comparison at a significance level of P < 0.05.

3  Results

3.1 Seedling Growth, Total Biomass, and RWC

Compared to the well-watered controls, the samples treated with PEG-induced drought showed significantly decreased shoot length but increased root length (Fig. 1A). Shoot biomass was significantly reduced by the drought treatment, whereas root biomass showed the opposite trend. The ratio of root-to-shoot biomass was therefore enhanced by drought (Fig. 1B). Under conditions of drought stress, seedling RWC decreased sharply, whereas that in the control seedlings showed no significant change during the 3 days of the experiment (Fig. 1C).

Figure 1: Mean (± SE) root length (RL) and shoot length (SL) (A), root weight (RW), shoot weight (SW) and the ratio between root and shoot (ratio) (B), and relative water content (RWC) (C) under drought stress treatment
Notes: Different lowercase letters indicate significant differences between treatments overall and asterisks (*) represent significant differences between well-watered (CK) and water-stressed (TR) plants on corresponding days (P < 0.05).

3.2 MDA Content and Electrolyte Leakage

Compared to the control plants, MDA content in the drought-treated seedlings was significantly higher (Fig. 2A). During the 3-day treatment, MDA content in drought-treated seedlings increased from day 0 to day 2, then decreased from day 2 to day 3. Electrolyte leakage increased significantly on all 3 days under PEG-induced drought conditions, whereas it did not change in the controls (Fig. 2B).

Figure 2: Mean (± SE) MDA content(A) and electrolyte leakage(B) under drought stress treatment
Note: Different lowercase letters indicate significant differences between treatments overall, and asterisks (*) represent significant differences between well-watered (CK) and water-stressed (TR) plants on corresponding days (P < 0.05).

3.3 Activities of Antioxidant Enzymes and Amylase

Compared to the well-watered seedlings, SOD activity in the drought-treated seedlings was significantly higher on days 2 and 3 (Fig. 3A). Drought-stressed plants experienced a significant increase in POD activity compared to the well-watered controls on days 1 and 2 (Fig. 3B). CAT activity in the drought-treated seedlings was sharply elevated compared to the controls (Fig. 3C). Drought-stressed plants showed a significant increase in amylase activity compared to the well-watered controls over the 3 days of the experiment (Fig. 3D). During the 3-day period, SOD, POD, CAT and amylase activities of drought-stressed plants were highest on day 2.

Figure 3: Mean (± SE) the SOD activity (A), POD activity (B), CAT activity (C) and amylase activity (D) under drought stress treatment
Note: Different lowercase letters indicate significant differences between treatments overall, and asterisks (*) represent significant differences between well-watered (CK) and water-stressed (TR) plants on corresponding days (P < 0.05).

3.4 Osmotic Regulators

Drought stress had stimulatory effects on soluble sugar, soluble protein and Pro contents (Figs. 4A–4C). Soluble sugar content of drought-stressed seedlings was significantly higher than that of controls on days 1 and 2. Compared to the controls, soluble protein content was enhanced by drought treatment on all days, whereas Pro content increased on days 2 and 3. During the 3 days of the experiment, soluble sugar, soluble protein and Pro contents in the drought-treated seedlings were highest on day 2.

Figure 4: Mean (± SE) of the soluble sugar content (A), the soluble protein content (B), and Pro content (C) under drought stress treatment
Note: Different lowercase letters indicate significant differences between treatments overall, and asterisks (*) represent significant differences between well-watered (CK) and water-stressed (TR) plants on corresponding days (P < 0.05).

3.5 Metabolic Changes Caused by Drought

3.5.1 Identification of Differentially Expressed Metabolites

GC–MS was used for maximal separation of the wide range of metabolites present in the drought-treated seedlings. In total, 1017 differentially expressed metabolites were identified in the drought-treated compared to control treatments (592 increased and 425 decreased) comprising organic acids, sugars, sugar alcohols, fatty acids, amino acids, flavones, sterols and phospholipids, as identified using the NIST library.

PCA was performed using the six individual replicates from each comparison group. The two principal components could explain 54.1% and 54.7% of the variation between drought-treated and control seedlings for the negative and positive ESI modes, respectively (Figs. 5A and 5B). By OPLS-DA, the two principal components explained 53.4% and 50.0% of the variation between treated and control seedlings for the negative and positive ESI modes, respectively (Figs. 5C and 5D). To test for model reproducibility and model fit, response-sorting tests of the OPLS-DA model, namely, fixed X matrix and previously defined Y matrix variable classifications (i.e., 0 or 1) were randomly arranged. The OPLS-DA model was established to obtain the corresponding stochastic model of R2 and Q2 values. With the original model of R2Y and Q2Y linear regression, the regression line and the Y axis intercept values (R2 and Q2) were used to measure whether the model was over-fitted (Figs. 5E and 5F). The model remained valid after cross-validation.

Figure 5: Principal component analysis (PCA) score plots of all samples (ESI-and ESI+) for the negative (A) and positive (B) ESI modes, PLS-DA score plots for the negative (C) and positive (D) ESI modes, permutation test for the negative (E) and positive (F) ESI modes. Each data point represents an independent sample

3.5.2 Metabolic Profiles and key Metabolites Affected by Drought

For the metabolomics analysis, 32 core metabolites of Tartary buckwheat were identified and quantified (Table 1, Fig. 6). A heat map showed overall changes in the 32 metabolites under normal vs. drought conditions (Fig. 7). Among them, quercetin, isovitexin, cyanidin 3-O-beta-D-glucoside, L-arginine, glycerophosphocholine, L-aspartic acid, tryptamine, (-)-epicatechin, taurine, quercetin 3-O-beta-D-glucopyranoside, N-acetyl-D-phenylalanine, carprofen, 2-deoxy-alpha-D-ribopyranose, 1-aminocyclobutane carboxylic acid, dimethyl phthalate, L-asparagine, and 2-dehydro-D-gluconic acid accumulated in the drought-treated seedlings (Figs. 6 and 7). The compounds 3-methoxytyramine, 2, 6-diaminopimelic acid, citric acid, UDP-alpha-D-glucose, adenosine, keto-D-fructose, enterolactone, L-pipecolic acid, erbstatin analog, vanillylmandelic acid, UMP, fumaric acid, CMP, galactitol, and thio-m-toluthioamide showed higher accumulations in the well-watered seedlings (Figs. 6 and 7).

Figure 6: Metabolites changes in Tartary buckwheat at 2 days after treatment. Green histograms correspond to the control and blue histograms to the treatment

Figure 7: Heat map of changes in 32 metabolites in Tartary buckwheat at 2 days after treatment

Results show log2 fold change ratios; red indicates an upregulation; green indicates a down-regulation. Color intensity codes are given in the color bar at the right side of the figures. Different clusters containing the metabolites with the same behavior are outlined.

3.5.3 Metabolic Pathways Affected by Drought

The 32 core metabolites were enriched in 29 metabolic pathways (Table 2). The pathways included alanine, aspartate and glutamate metabolism, flavone and flavonol biosynthesis, lysine biosynthesis, citrate cycle (TCA cycle), anthocyanin biosynthesis, aminoacyl-tRNA biosynthesis, pyrimidine metabolism, zeatin biosynthesis, and purine metabolism. With fold change > 0.2 as the screening condition, the KEGG metabolic pathways taurine and hypotaurine metabolism, flavor and flavor biosynthesis, indole alkaline biosynthesis, and alanine, aspartate and glutamate metabolism were most highly represented.

4  Discussion

Tartary buckwheat is an important pseudocereal crop worldwide. Due to its limited growing area, Tartary buckwheat is typically exposed to a number of environmental stresses, such as high light/UV radiation, cold, drought, salt, and heavy metals. Drought severely affects growth and yield of Tartary buckwheat in agricultural production. Unfortunately, little is known about the effect of drought on Tartary buckwheat and its response to water stress. This motivated the current study.

4.1 Seedling Growth, Physiological and Biochemical Indexes

Previous research has shown the many adverse effects of drought stress on plant growth, physiological metabolism and biochemical parameters [41], ultimately resulting in yield reduction or even plant death [42,43] . We found a significant reduction in shoot biomass and shoot length of Tartary buckwheat plants under drought, indicating inhibition of plant shoot growth under the drought treatment, as reported in wheat and other plants [44]. However, root length increased under drought, confirming the accessibility of moisture deep in the soil and ensuring adaptation to drought stress. The observed decline of RWC under water stress can damage the plant’s ability to maintain its water balance [45]. Stressed plants exhibit excess generation of ROS which can cause membrane lipid peroxidation, leading to cell damage or death [46]. Enhanced H2O2, MDA contents and electrolyte leakage are common symptoms of membrane damage under water stress [45,47]. MDA is the final product of membrane lipid peroxidation and indicates cell membrane injury [35], and electrolyte leakage is considered a marker of membrane damage under drought [47]. In our study, MDA and electrolyte leakage were significantly increased in the treated compared to control seedlings, indicating that drought can result in membrane damage; significant differences in MDA content and electrolyte leakage appeared at 24 h into the drought treatment.

To alleviate drought-induced oxidative stress, antioxidant enzymes function as ROS scavengers [47]. SOD is the first enzyme to play a key role in oxidative defense by catalyzing superoxide free radical dismutation of O2− to H2O2 [48]; the generated H2O2 is then eliminated by CAT and POD [47,49] through its catalysis to H2O and O2 [45]. SOD activity in Tartary buckwheat increased significantly 24 h after drought induction, and continued to increase to 48 h of drought. Significant increases in POD and CAT activity have been shown in stressed plants [45]. POD and CAT activity in Tartary buckwheat showed the same trend as SOD activity under the drought treatment. Increases in SOD, POD and CAT activities in Tartary buckwheat after rehydration could reflect an adaptation toward alleviating oxidative stress and improving growth, as in other plants [50].

Typically, osmotically active solutes such as Pro, soluble proteins and soluble sugars can mitigate the oxidative damage caused by ROS under water stress [51]. These molecules stimulate plants to absorb more water to withstand dehydration by maintaining turgor, buffering against ROS and protecting the redox balance [51] . Among them, Pro exhibits antioxidant activity and promotes redox to maintain homeostasis, thereby reducing lipid peroxidation [52]. The Pro content of Tartary buckwheat showed a significant and remarkable increase under drought stress 48 h after drought initiation. Plant accumulation of soluble proteins and soluble sugars under drought stress has been found in many studies [48]. Here, the contents of these two osmotic-adjustment substances showed the same trends, mitigating drought damage.

4.2 Secondary Metabolites and Pathways

Plants tend to biosynthesize specialized metabolites to adapt to drought stress. We identified 32 core metabolites that were enriched in 29 metabolic pathways in a Tartary buckwheat variety CQ1. Of these core metabolites, taurine accumulated significantly in response to drought stress (Fig. 6). In plants, an increase in taurine reflects induction of the taurine and hypotaurine metabolic pathway (Table 2). Taurine plays an important role in adjusting normal physiological functions to confer stress resistance via osmotic regulation or antioxidation [53].

Flavonoids are postulated to contribute antioxidant protection, due to their hydroxyl groups, the presence of double bonds, and their predisposition toward glycosylation and methylation [54]. Quercetin is considered one of the best electron donors among the flavonoids as it has an ortho-dihydroxy pattern in the B-ring of the flavonoid skeleton [55]. In Tartary buckwheat, we found quercetin to be associated with quercetin 3-O-beta-D-glucopyranoside in flavone and flavonol biosynthesis improved under drought conditions (Fig. 6, Table 2), and the increase in quercetin concentration conferred greater H2O2− scavenging activity [56], which might be a strategy for drought tolerance due to the powerful antioxidant activity of flavonoids [57].

Tryptophan is the substrate for alkaloids, indoles, auxin, phytomelatonin, glucosinolates and phytoalexins, and it therefore plays an important role in the plant stress response [58]. Tryptamine is biosynthesized from tryptophan by trytophan decarboxylase and oxidized by monoamine oxidase [59]. Tryptamine is one of the major contributors to the water-stress response in barley [60]. In rice, the indole alkaloid tryptamine inhibited infection by Magnaporthe oryzae as a key factor in light-enhanced resistance [59]. In Tartary buckwheat, tryptamine increased through the indole alkaline biosynthesis pathway under drought conditions.

The accumulation of amino acids has been reported in various plants under abiotic stress. They are considered to have a beneficial effect during stress acclimation in some species, or to indicate cell damage in others [61,62]. In maize, arginine was considered an osmoprotectant as it increased under water stress [61]. In a Fagus sylvatica L. population, aspartic acid together with valine, isoleucine, threonine, serine and glycine, increased in response to water stress [63]. In our study, L-aspartic acid and L-asparagine involved in alanine, aspartate and glutamate metabolism were upregulated under drought conditions, whereas fumaric acid decreased.

5  Conclusion

To our knowledge, this is the first study of physiological responses and identification of the metabolic profile of Tatary buckwheat under drought stress. ROS markers, including MDA content and electrolyte leakage, activities of the antioxidant enzymes SOD, POD and CAT, and contents of the osmotic regulators soluble sugar, soluble protein and Pro all increased in response to drought stress in Tatary buckwheat, along with metabolites that included organic acids, sugars, sugar alcohols, fatty acids, amino acids, flavones, sterols and phospholipids. Taurine and hypotaurine metabolism, flavone and flavonol biosynthesis, indole alkaline biosynthesis, alanine, aspartate and glutamate metabolism might be the main metabolic pathways used to cope with drought stress. The presented information on the metabolic profile should provide new insights into the biochemical pathways underlying drought tolerance in Tartary buckwheat plants.

Authorship: Conceptualization, Yan Wan, Dabing Xiang and Liang Zou; Data curation, Yan Wan, Yuan Liang, Xuxiao Gong, and Jianyong Ouyang; Formal analysis, Jingwei Huang and Xiaoyong Wu; Funding acquisition, Yan Wan, Dabing Xiang and Liang Zou; Investigation, Liang Yuan, and Changying Liu; Methodology, Yuan Liang, Xuxiao Gong, and Xiaoning Cao; Project administration, Xuxiao Gong and Liang Yang, and Jiangyong Ouyang; Resources, Dabing Xiang, Qi Wu and Gang Zhao; Supervision, Dabing Xiang, Gang Zhao and Liang Zou; Visualization, Liang Zou; Writing–original draft, Yan Wan, Yuan Liang, Xuxiao Gong, Jianyong Ouyang,; Writing–review & editing, Xueling Ye, Changying Liu, Xiaoyong Wu, Qi Wu and Gang Zhao.

Funding Statement: We acknowledge the Project of National Key Research and Development Program of China (2020YFD1001403), China Agriculture Research System (CARS-07-B-1), Science & Technology Department of Sichuan Province (2022YFQ0041), the National Natural Science Foundation of China (31601260, 32160428), Innovative Training Program for College Students (S202111079058) and Special Research Fund from Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs (2020CC012) to facilitate the research.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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