Comparative Analysis of Flavonoid Metabolites in Foxtail Millet (Setaria Italica) with Different Eating Quality

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Article Comparative Analysis of Flavonoid Metabolites in Foxtail Millet (Setaria italica) with Different Eating Quality

Yakun Zhang 1,†, Jianhua Gao 2,†, Qianru Qie 1, Yulu Yang 1, Siyu Hou 1, Xingchun Wang 2, Xukai Li 2,* and Yuanhuai Han 1,*

1 College of Agriculture, Shanxi Agricultural University, Jinzhong 030801, China; [email protected] (Y.Z.); [email protected] (Q.Q.); [email protected] (Y.Y.); [email protected] (S.H.) 2 College of Life Sciences, Shanxi Agricultural University, Jinzhong 030801, China; [email protected] (J.G.); [email protected] (X.W.) * Correspondence: [email protected] (X.L.); [email protected] (Y.H.) † These authors contributed equally to the paper.

Abstract: Foxtail millet (Setaria italica) is an important minor cereal crop in China. The yellow color of the de-husked grain is the most direct aspect for evaluating the foxtail millet quality. The yellow pigment mainly includes carotenoids (lutein and zeaxanthin) and flavonoids. To reveal the diversity and specificity of flavonoids in foxtail millet, we chose three high eating quality and two poor eating quality varieties as research materials. A total of 116 flavonoid metabolites were identified based on Ultra Performance Liquid Chromatography-Electrospray Ionization-Tandem Mass Spectrometry (UPLC-ESI-MS/MS) system. The tested varieties contained similar levels of flavonoid metabolites, but with each variety accumulating its unique flavonoid metabolites. A total

of 33 flavonoid metabolites were identified as significantly discrepant between high eating quality and poor eating quality varieties, which were mainly in the flavonoid biosynthesis pathway and one

Citation: Zhang, Y.; Gao, J.; Qie, Q.; of its branches, the flavone and flavonol biosynthesis pathway. These results showed the diversified Yang, Y.; Hou, S.; Wang, X.; Li, X.; components of flavonoids accumulated in foxtail millets and laid the foundation for further research Han, Y. Comparative Analysis of on flavonoids and the breeding for high-quality foxtail millet varieties. Flavonoid Metabolites in Foxtail Millet (Setaria italica) with Different Keywords: foxtail millet; flavonoid metabolome; functional components; UPLC-ESI-MS/MS Eating Quality. Life 2021, 11, 578. https://doi.org/10.3390/life11060578

Academic Editor: Stefania Lamponi 1. Introduction Foxtail millet (Setaria italica (L.) P. Beauv), a member of the family of Poaceae, origi- Received: 25 May 2021 nated in the Yellow River basin in China more than 11,000 years ago [1]. Foxtail millet Accepted: 15 June 2021 plays a nutritional and functional role in the diet for many people [2]. It is nutritionally Published: 18 June 2021 comparable to some other major cereals. For example, it has a higher content of protein, lipids, and lower carbohydrate content than some cereals (wheat and maize) [3]. Compared Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in with rice, foxtail millet has double protein content, fourfold minerals and fat, and triple published maps and institutional affil- calcium [4]. Ample evidence showed that increasing the consumption of foxtail millet was iations. associated with a lower risk of diabetes [5]. Therefore, foxtail millet is gaining increasing attention among consumers. Generally, the yellow color of the de-husked grain is the most direct indicator for consumers to evaluate the millet quality. Many compounds, including carotenoids (lutein and zeaxanthin), flavonoids, are predominant yellow pigments in plants and are believed to confer nutritional and pharmacological benefits to plants. There have Copyright: © 2021 by the authors. been preliminary studies on carotenoids in foxtail millet [6,7]. However, a systematic Licensee MDPI, Basel, Switzerland. This article is an open access article understanding of flavonoid diversity in foxtail millet is still lacking. distributed under the terms and Foxtail millets are rich in flavonoids [8,9], a large group of natural products with conditions of the Creative Commons variable phenolic structures, containing flavone, flavanone, flavonols, isoflavone, antho- Attribution (CC BY) license (https:// cyanins, and so on. Flavonoids play essential roles in plants in forming the color and creativecommons.org/licenses/by/ flavor of flowers and fruits, and resistance against ultraviolet B (UV-B), diseases, and pests 4.0/).

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damages [10–12]. For example, the accumulation of flavonoids led to the deep yellow col- oration of red Chinese pears after methyl jasmonate (MeJA) treatment. It also affected the appearance of radishes with multiple colors [13,14]. Furthermore, flavonoids have potential benefits for human health, including antioxidation, anti-inflammatory, anti-cholinesterase, anticancer and heart diseases, and countering antibiotic resistance [15–17]. The flavonoid biosynthesis pathway has been well understood in Arabidopsis thaliana, Zea mays, and Petunia hybrida, providing a basis for studying flavonoids in foxtail millet [18,19]. Metabolomics has been introduced as a novel technology to analyze the microbial, plant, and animal metabolomes and is considered the bridge linking the genomes and the phenotypes [20]. The high sensitivity and fast scanning speed make ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) a preferred method of component analysis, in which the multiple reaction monitoring (MRM) mode in triple quadrupole mass spectrometry (UPLC-QQQ-MS/MS) is often used to eliminate the in- fluence of matrix effects in quantitative analyses [21]. Various metabolites and the corre- sponding genetic characteristics in plants have been identified based on this metabolic path-based approach, such as the identification of glucosides in Arabidopsis leaves [22], the synthesis and regulation of flavonoids in rice [23], and the comparison of metabolism substances in plants [24]. This study aimed to characterize flavonoid metabolites in foxtail millets and explore the diversity of flavonoid metabolites among different varieties using UPLC-ESI-MS/MS. It is expected that our work could provide a basis for further understanding of the quality of foxtail millet and pave the way for the breeding of better varieties with high flavonoids.

2. Materials and Methods 2.1. Plant Materials Five foxtail millet varieties were selected as experimental materials, which were “Jingu21” (JG21), “Qinzhouhuang” (QZH), “Yugu1” (YG1), “Daobaqi” (DBQ) and “Niu- maobai” (NMB). JG21 is an elite variety from Shanxi province with excellent quality and the largest growing area in China. QZH is another outstanding variety bred from an elite landrace that originated from Qinxian County in Shanxi province. YG1 is an elite variety originating from Henan province in China, the most inﬂuential species in North China, and has a high-quality reference genome sequence [25]. Both DBQ and NMB are landrace with poor eating quality. All the foxtail millet varieties were planted in the experimental ﬁeld of Shanxi Agricultural University (37.42 N, 112.59 E), Taigu, China, in May 2018 and harvested in October 2018 at full maturity. The region has a semiarid climate, with an average annual temperature of 11.91 ◦C and annual precipitation of 396.24 mm in 2018.

2.2. Photo Recording and Color Determination The husk of foxtail millet seeds was removed using small hulling separators. For each variety, we selected well-developed grains for the experiments. The de-husked millets were put into containers for taking photos. The colors of these millets were measured by a colorimeter (X-Rite VS450, Big Rapids, Michigan, USA). After determining the color, the freeze-dried seed was crushed using a mixer mill (Retsch MM400, Dusseldorf, North Rhine-Westphalia, Germany) with a zirconia bead for 1.5 min at 30 Hz. The powder was also photographed, and its color was measured. Three replicates were carried out. The values of color parameters “L,” “a,” and “b,” which represent the luminosity, redness, and yellowness, respectively, were collected. The color contribution index (CCI) values were calculated according to CCI = 1000 × a/(L × b) [26].

2.3. Preparation and Extraction of Samples for Metabolomics Analysis The preparation and extraction of samples were performed as previously described [27]. One hundred mg foxtail millet powder was weighed and extracted overnight at 4 ◦C with 1.0 mL 70% aqueous methanol (including 0.1 mg/L lidocaine or 0.1 mg/L acyclovir) to ensure sufﬁcient reaction. After that, the samples were centrifuged at 4 ◦C and 14,000 r Life 2021, 11, 578 3 of 14

for 5 min. The supernatant was collected and centrifuged again. Then, the water-soluble extracts were absorbed on a CNWBOND Carbon-GCB SPE Cartridge (ANPEL, Shanghai, China) and ﬁltrated with SCAA-104 (ANPEL, 0.22 µm pore size, Shanghai, China), for standby application.

2.4. Ultra-High-Performance Liquid Chromatography (UPLC) Conditions The extracted samples were analyzed as described by Wang et al. [28]. A UPLC-ESI- MS/MS system (UPLC, Shim-pack UFLC SHIMADZU CBM30A system; MS, Applied Biosystems 6500 Q TRAP, Foster City, USA) equipped with a C18 chromatographic column (Waters ACQUITY UPLC HSS T3, 2.1 mm × 100 mm, 1.8 µm) was used for the analysis. The solvent systems contained mobile phase A (0.04% acetic acid in water) and mobile phase B (0.04% acetic acid in acetonitrile). The gradient program (Mobile phase A: Mobile phase B) was performed as follows: 95:5 (v/v) at 0 min, 5:95 (v/v) at 11.0 min, 5:95 (v/v) at 12.0 min, 95:5 (v/v) at 12.1 min, and 95:5 (v/v) at 15.0 min; ﬂow rate 0.40 mL/min. The column temperature was kept at 40 ◦C, and the injection volume was set to 2 µL. The UPLC efﬂuent was input into an ESI-triple quadrupole-linear ion trap (Q TRAP)-MS and analyzed further.

2.5. Electrospray-Triple Quadrupole-Linear Ion Trap Mass Spectrometry (ESI-Q TRAP-MS/MS) System The mass spectrometry (MS) followed the method described by Chen et al. [29]. The Linear ion trap (LIT) and triple quadrupole (QQQ) scans were performed on a triple quadrupole-linear ion trap mass spectrometer (Q TRAP, AB Sciex, Foster City, CA, USA), API 6500 Q TRAP LC/MS/MS System. This system was equipped with an ESI Turbo Ion-Spray interface, with both positive and negative ion modes controlled by Analyst 1.6. The operating parameter of ESI was an ion source (turbo spray, 550 ◦C, 5500 V). Ion source gas I (GS I), ion source gas II (GS II), and curtain gas (GRU) were set at 55, 60, and 25 psi, respectively. The collision gas (CAD) was set to high. Polypropylene glycol solutions with concentrations at 10 µmol/L and 100 µmol/L were used for instrument debugging and mass calibration in QQQ and LIT modes, respectively. QQQ scanning was performed in a multiple reaction monitoring (MRM) mode with the collision gas (nitrogen) set to 5 psi. The de-clustering potential (DP) and collision energy (CE) for each MRM transition were completed by further optimizing DP and CE. The speciﬁc MRM for each time quantum was detected according to the metabolites eluted in this period.

2.6. Qualitative and Quantitative Analysis of Metabolites The qualitative analysis of the primary and secondary spectral data of the MS was analyzed based on the MWDB database (Metware Biotechnology Co., Ltd. Wuhan, China). It removed the repetitive signals containing K+, Na+, NH4+, and fragment ions with larger molecular weight. The quantitative analysis was performed using MRM analysis of QQQ mass spectrometry. After induced ionization in the collision chamber, the precursor ions were fractured into fragments and then ﬁltered through the triple quadrupole in which the desired characteristic fragments were selected. After obtaining the mass spectrometry data, the chromatographic peaks of all the targets were integrated for quantitative analysis [28,30].

2.7. Quality Control Analysis of Samples The stability of the instruments ensured the repeatability and reliability of the data. The quality control samples were composed of mixing equal parts extractions of three groups of different millet varieties. The method of processing and testing samples was the same as that of analyzing the samples. A quality control sample was inserted between 10 testing samples to guarantee the repeatability of the whole analytic process. Life 2021, 11, x FOR PEER REVIEW 4 of 14

2.8. Statistical Analysis

Life 2021, 11, 578Three biological replicates were carried out for each experiment. The significant dif- 4 of 14 ference between the groups was determined using a one-way analysis of variance (ANOVA) and Duncan’s multiple range test (p < 0.05). Principal components analysis (PCA), orthogonal partial2.8. Statistical least Analysissquares discrimination analysis (OPLS-DA), hierarchical clustering analysis (HCA),Three and biological the volcano replicates plot were drawing carried were out for carried each experiment. out using ggplot2, The signiﬁcant muma, pheatmap packagesdifference of between R (http://www.r-project.org, the groups was determined accessed using a one-wayon 2019) analysis [31–33]. of variance (ANOVA) and Duncan’s multiple range test (p < 0.05). Principal components analysis (PCA), orthogonal partial least squares discrimination analysis (OPLS-DA), hierarchical 3. Results clustering analysis (HCA), and the volcano plot drawing were carried out using ggplot2, 3.1. The Millet Color muma,of Different pheatmap Varieties packages of R (http://www.r-project.org, accessed on 2019) [31–33].

It is generally believed3. Results the darker the yellow color is, the better the eating quality of the millet [34]. To explore3.1. The Milletthe color Color ofvariation Different Varieties of different quality foxtail millet varieties, we conducted the colorIt measurement is generally believed of the the darkergrains the and yellow their color matching is, the better powder. the eating In qualitythe of tested varieties, thethe kernels millet [of34]. JG21, To explore QZH, the and color YG1 variation were of differentof different quality shades foxtail milletof yellow. varieties, we NMB was a white milletconducted variety, the color and measurement DBQ was a of millet the grains variety and their with matching greyish powder. green Incolor the tested varieties, the kernels of JG21, QZH, and YG1 were of different shades of yellow. NMB was (Figure 1A). The powdera white of millet JG21 variety, showed and DBQ the wasdeepest a millet yellow, variety and with greyishDBQ and green NMB color (Figurewere 1A). both lighter yellow The(Figure powder 1B). of JG21 showed the deepest yellow, and DBQ and NMB were both lighter yellow (Figure1B). (A)

1 cm (B)

JG21 QZH YG1 DBQ NMB

Figure 1. Samples of foxtail millet varieties. (A) The foxtail millet kernel samples. (B) The foxtail millet powder samples. TheFigure top of 1. (B )Samples shows the of powder foxtail of 5millet foxtail varieties. millet varieties (A) takenThe byfoxtail the camera, millet and kernel the bottom samples. of (B) ( showsB) The the foxtail color of foxtailmillet millet powder powder samples. scanned byThe the top colorimeter. of (B) shows the powder of 5 foxtail millet varieties taken by the camera, and the bottom of (B) shows the color of foxtail millet powder scanned by the colorimeter. To accurately evaluate the color of these millets, color parameters “L” (luminosity), “a” To accurately evaluate(redness), andthe “b”color (yellowness) of these valuesmillets, were color measured parameters (Figure2 “L” and Table(luminosity), S1). Signiﬁcant differences (p < 0.05) were detected in the three parameters of the ﬁve foxtail millet varieties. “a” (redness), and “b”NMB (yellowness) had the highest values “L” value,were meaningmeasured the (Figure maximum 2 and brightness, Table S1). followed Signif- by QZH, icant differences (p

Life 2021, 11, x FOR PEER REVIEW 5 of 14

the visual observation and showed the large discrepancies in millet color among varieties with different eating quality.

(A) Life 2021, 11, x FOR PEER REVIEW (B) (C) (D) 5 of 14 d e 10.0 e c e d 4 d 60 b d c c a c 40 7.5 b b b b Life 2021, 11, 578 3 5 of 14 40 the visual observation and showed the large discrepancies30 in millet colora among varieties a L* witha* different5.0 eating quality. a 2 b* 20 CCI 20 2.5 (A) (B) (C) 10 (D) 1 e 10.0 e c d e 4 d 60 b c d c 0 a 0.0 c d b 40 0 0 7.5 b 3 b b 1 21 1 H B 2 1 H B 40 BQ BQ 30 1 a Q a 1 G YG YG M ZH J QZ D NM 5.0 JG QZ Da N B G2 L* a* b* JG2 YG1 QZHCCID 2 NMB J YG1 Q DBQ NMB Varieties Varieties 20 20 2.5 Varieties Varieties 10 1 0 0.0 0 0 Figure 2. Barplot of color parameters in different1 foxtail millet varieties. (A) Luminosity of millet color. (B) Redness of 21 1 H B 2 1 H B BQ BQ 1 Q 1 G YG YG M ZH J QZ D NM JG QZ D N B G2 millet color. (C) Yellowness of millet color. (D) A composite indicatorJG2 YG1 ofQZH milletD NMB color calculatedJ YG1 QaccordingDBQ NMB to CCI = 1000 × Varieties Varieties Varieties Varieties a/(L × b). The x-axis shows the variety name, and the y-axis shows the value of the parameter. The lowercase letters above the histogramFigureFigure 2. Barplot indicated2. Barplot of color of thecolor parameters statistical parameters in differentsignificancein different foxtail foxtail at millet themillet varieties.level varieties. of (0.05 A(A)) Luminosity Luminosity(p < 0.05). ofof milletmillet color.color. ( B)) Redness Redness of of millet color.millet (C) Yellowness color. (C) Yellowness of millet color. of millet (D )color. A composite (D) A composite indicator indicator of millet of color millet calculated color calculated according according to CCI to =CCI 1000 = 1000× a/(L × × b). The x-axisa/(L × b). shows The x-axis the variety shows the name, variety and name, the and y-axis the showsy-axis shows the value the value of the of the parameter. parameter. TheThe lowercase letters letters above above the the histogram indicated 3.2.the statistical Flavonoid significance Metabolit at the leveles Profiles of 0.05 (p in< 0.05). Millets of Different Varieties histogram indicated the statistical significance at the level of 0.05 (p < 0.05). A total of 116 flavonoid metabolites (Table S2) from the five foxtail millet varieties 3.2. Flavonoid Metabolites Profiles in Millets of Different Varieties were3.2. Flavonoid identified Metabolites using ProfilesUPLC-ESI-MS/MS in Millets of Different to obta Varietiesin the profile of flavonoid metabolites in A total of 116 flavonoid metabolites (Table S2) from the five foxtail millet varieties foxtailwereA identified totalmillet. of 116The using flavonoid heatmap UPLC-ESI-MS/MS metabolites showed to obtathe (Tablein relative the S2) profile from content of the flavonoid five of foxtail flavonoid metabolites millet metabolites varietiesin after ho- mogenizationwerefoxtail identified millet. The using(Figure heatmap UPLC-ESI-MS/MS showed3A). The the relative 116 to flavonoidcontent obtain of the flavonoid profilemetabolites metabolites of flavonoid can after metabolitesbe ho- divided into eight groups,inmogenization foxtail including millet. (Figure The 56 heatmap 3A). flavones, The showed 116 23flavonoid flavonols, the relative metabolites 11 content flavanone, can of be flavonoid divided 6 polyphenol, metabolitesinto eight 3 after anthocyanins, 2 homogenizationgroups, including (Figure 56 flavones,3A). 23 The flavonols, 116 flavonoid 11 flavanone, metabolites 6 polyphenol, can be3 anthocyanins, divided into 2 eight proanthocyanidins,groups,proanthocyanidins, including 56 2 isoflavones, flavones, 2 isoflavones, 23 and flavonols, 13 otherand flavonoids 1113 flavanone,other (Figureflavonoids 6 polyphenol, 3B). Apigenin, (Figure 3 luteolin, anthocyanins, 3B). Apigenin, luteolin, naringenin,2 proanthocyanidins,naringenin, kaempferol, kaempferol, 2 isoflavones, chrysoeriol, chrysoeriol, andnaringen 13 other naringenin, and flavonoids theirin, derivatives and (Figure their 3madeB). derivatives Apigenin, up most of luteolin, made up most of thenaringenin,the total total flavonoid kaempferol, metabolites, metabolites, chrysoeriol, mainly naringenin,mainlyinvolved fourinvolved and main their clusters. four derivatives mainThe heat clusters. made map upalso most The ofheat map also theshowed total flavonoida strong contrast metabolites, in the relative mainly content involved of metabolites four main clusters.between NMB The heatand the map also showedother four a varieties.strong Thecontrast relative in content the ofrelative metabolites content in JG21 of was metabolites similar to QZH, between and NMB and the showed a strong contrast in the relative content of metabolites between NMB and the other otherYG1 fourwas similar varieties. to DBQ. The relative content of metabolites in JG21 was similar to QZH, and four varieties. The relative content of metabolites in JG21 was similar to QZH, and YG1 YG1 was similar to DBQ. was(A) similar to DBQ. (B)

pmb0701 pme1580 pmb2976 pmb0645 3 pma6371 pmb0588 Anthocyanins pmf0265 3 pme0088 pmf0277 (A) pme3129 (B) pmf0109 2 pme1478 pmb0639 pme0089 pme1662 pme0371 pmf0151 pmb0701 pme0376 1 pme1580 pme2957 pmb2976 Flavanone pmf0057 11 pmf0058 pmb0645 3 pme3473 pma6371 pmb0665 pmb0588 Anthocyanins pmb0835 0 pmf0265 3 pme1518 pme1550 pme0088 pme1611 pmf0277 pme3211 pme3129 pme3267 pme1622 pmf0109 2 pme2457 -1 pme1478 pma6496 pmb0639 pmb3024 Flavone pmf0247 pme0089 56 pme1598 pme1662 pme0374 pme0371 pma6647 pme0363 -2 pmf0151 pmf0208 pme0376 1 pmf0204 pme2957 Flavanone pmb0569 pmf0057 11 pmb0618 pmb0607 pmf0058 pmb0603 pme3473 pme0197 -3 pmb0711 pmb0665 pma0214 pmb0835 Flavonol0 pma6639 pme1518 23 pmf0375 pmb0595 pme1550 pma1108 pme1611 pma6389 pme3211 pmb0608 pme3267 pmb0622 pma0795 pme1622 pmb0736 pme2457 -1 pma0779 pma6496 pmc1990 pmb3024 Flavone pme3461 56 pmb3013 pmf0247 pmb0720 pme1598 Isoflavone 2 pma0760 pme0374 pmb0712 pmb0713 pma6647 pma6558 pme0363 -2 pme3288 pmf0208 pmb0605 pme0433 pmf0204 pme1500 pmb0569 pmb3052 pmb0618 pmb3032 pmb2957 pmb0607 pmf0614 pmb0603 pmf0617 Otherpme0197 Flavonoids-3 pmb3042 13 pme1605 pmb0711 pme0369 pma0214 Flavonol pme0002 pma6639 23 pme2950 pmf0011 pmf0375 pmb0693 pmb0595 pmf0234 pma1108 pmb0716 pmf0381 pma6389 pmb3044 pmb0608 pma6586 pmb0622 pmf0548 pma0795 pma1087 Polyphenol pmb0745 pmb0736 6 pmb3053 pma0779 pmb3031 pma6518 pmc1990 pme0355 pme3461 pmf0279 pmb3013 pme2977 pme2493 pmb0720 Isoflavone 2 pme0361 pma0760 pme2898 pmb0712 pmf0274 pmb0713 pmb0602 pmf0458 pma6558 pme3263 Proanthocyanidinspme3288 pmb3894 pmb0605 2 pmb0277 pme0433 pme0431 pme3401 pme1500 pme2478 pmb3052 pmb0322 pme3237 pmb3032 pme1824 pmb2957 pmb0600 pmf0614 pme3227 pmf0617 Other Flavonoids pmf0179 pmb3042 13 pmb0653 pme1521 pme1605

JG21-3 JG21-1 JG21-2 pme0369 NMB-3 NMB-1 NMB-2 QZH-1 QZH-2 QZH-3 YG1-3 YG1-1 YG1-2 DBQ-1 DBQ-2 DBQ-3 pme2247 pme0002 pme2950 0 1020304050 pmf0011 pmb0693 pmf0234 pmb0716 pmf0381 pmb3044 pma6586 pmf0548 pma1087 Polyphenol pmb0745 6 pmb3053 pmb3031 pma6518 Figure 3. Clustering heat map of all flavonoidpme0355 metabolites. (A) Heatmap of all the detected flavonoid pmf0279 pme2977 pme2493 pme0361 metabolites in the high and poor foxtailpme2898 millet varieties. (B) The numbers and types of flavonoid pmf0274 pmb0602 pmf0458 pme3263 Proanthocyanidins metabolites in foxtail millet grains. pmb3894 2 pmb0277 pme0431 pme3401 pme2478 pmb0322 pme3237 pme1824 pmb0600 pme3227 pmf0179 pmb0653 pme1521 JG21-3 JG21-1 JG21-2

NMB-3 NMB-1 NMB-2 QZH-1 QZH-2 QZH-3 YG1-3 YG1-1 YG1-2 DBQ-1 DBQ-2 DBQ-3 pme2247 0 1020304050

FigureFigure 3.3.Clustering Clustering heat heat map map of all of flavonoid all flavonoid metabolites. metabolites. (A) Heatmap (A) ofHeatmap all the detected of all flavonoidthe detected flavonoid metabolitesmetabolites in in the the high high and and poor poor foxtail foxtail millet varieties.millet varieties. (B) The numbers (B) The and numbers types of and flavonoid types of flavonoid metabolitesmetabolites in in foxtail foxtail millet millet grains. grains.

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Life 2021, 11, 578 We conducted a multivariate statistical analysis of 116 6metabolites of 14 using PCA. A model with two principal components was constructed. The contribution rates of PC1 and PC2 were 35.88% and 26.78%, respectively. The total contribution rate reached 62.66% (FigureWe conducted 4). The afive multivariate varieties statistical were analysis separated of 116 clearly, metabolites and using the PCA. replicates A were compactly model with two principal components was constructed. The contribution rates of PC1 gathered,and PC2 were showing 35.88% and ideal 26.78%, repeatability respectively. The and total contributionreliability. rate For reached PC1, 62.66% four varieties (JG21, QZH, DBQ,(Figure 4and). The YG1) five varieties got similar were separated scores clearly,and were and the significantly replicates were separated compactly from NMB, indicating thegathered, metabolites showing ideal profiling repeatability of NMB and reliability. was notably For PC1, distinguished four varieties (JG21, from QZH, the other four varieties. DBQ, and YG1) got similar scores and were significantly separated from NMB, indicating Thisthe metabolites was consistent profiling of with NMB wasthe notably row-clustering distinguished fromof the the heatmap. other four varieties. For PC2, the score of NMB wasThis was close consistent to DBQ, with theand row-clustering JG21 was ofclose the heatmap. to QZ ForH, PC2,caused the score by oftheir NMB common was flavonoids. How- ever,close to YG1 DBQ, andwas JG21 located was close away to QZH, from caused the by other their common two high-quality flavonoids. However, varieties, which might be YG1 was located away from the other two high-quality varieties, which might be caused causedby the large by difference the large in the difference content of certain in the flavonoids. content of certain flavonoids.

Figure 4. The PCA score plot of flavonoid metabolites in different foxtail millet samples. Good_Si Figurerepresents 4. high-quality The PCA varieties, score andplot Bad_Si of flavonoid represents poor-quality metabolites varieties. in different foxtail millet samples. Good_Si represents high-quality varieties, and Bad_Si represents poor-quality varieties. 3.3. Pairwise Comparison of Flavonoid Metabolites Between High and Poor Eating Quality VarietiesBased on OPLS-DA Model 3.3. ToPairwise find differential Comparison metabolites of Flavonoid between high Metabolit and poores eating Between quality High varieties, and we Poor Eating Quality Vari- etiesBasedperformed the on OPLS-DA OPLS-DA model Model to produce pairwise comparisons. In this model, the systematic variation in variable X was decomposed into two parts: linear correlation to Y andTo orthogonality find differential to Y [35]. Itmetabolites can complete thebetween prediction high of sample and categoriespoor eating by quality varieties, we performedestablishing the the relational OPLS-DA model between model the to flavonoid produce metabolites pairwise and samplecomparisons. categories In this model, the sys- and maximize the distinction between groups mainly reflected in T1 (Figure5A). In this tematicstudy, the variation flavonoid metabolites in variable in high X was and poor-eating decomposed quality into millets two were parts: explicitly linear correlation to Y and orthogonalityseparated based on to the Y OPLS-DA [35]. It model,can complete showing a significantthe prediction difference of between sample the twocategories by establishing thegroups. relational The explanation model degree between of the principal the flavonoid component metabolites to the independent and variable sample X categories and max- and categorical variable Y was 86.8% (R2X) and 99.9% (R2Y), respectively. The prediction imizeindicator the of the distinction model on the between five foxtail milletgroups varieties mainly was 99.8% reflected (Q2 = 0.998). in T1 The (Figure OPLS- 5A). In this study, the flavonoidDA model was metabolites tested by permutation in high analysis and poor-eating (Figure5B). As thequality similarity millets gradually were explicitly separated based on the OPLS-DA model, showing a significant difference between the two groups. The explanation degree of the principal component to the independent variable X and categorical variable Y was 86.8% (R2X) and 99.9% (R2Y), respectively. The prediction indicator of the model on the five foxtail millet varieties was 99.8% (Q2 = 0.998). The OPLS- DA model was tested by permutation analysis (Figure 5B). As the similarity gradually decreased, the R2 and Q2 of the random model gradually decreased, indicating no over- fitting phenomenon in the original model. Therefore, these two groups could be used to screen differential flavonoid metabolites.

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Life 2021, 11, x FOR PEER REVIEW 7 of 14 decreased, the R2 and Q2 of the random model gradually decreased, indicating no over- ﬁtting phenomenon in the original model. Therefore, these two groups could be used to screen differential ﬂavonoid metabolites. (A) (B)

Good_Si 1.0 20 Bad_Si

DBQ-2DBQ-3 DQB-1 0.5 10

QZH-1J1QZH-2QZH-3G2 -2-3-1 0 t01

Y1G-3G-1G-2

-10 NBNMB-1M-3M-2 -0.5 0.0

-20 Q2Y

-1.0 R2Y -20 -10 0 10 20 R2X R2Y Q2Y RMSEE pre ort 0.2 0.4 0.6 0.8 1.0 0.999 0.998T1(27%) 0.015 1 2 Similarity(y,yperm) 0.868 Figure 5. PairwiseFigure 5.comparisonPairwise comparison based basedon OPLS-DA on OPLS-DA model. model. (A (A)) OPLS-DA model model plots plots for the for comparison the comparison group Good_Si group Good_Si vs. Bad_Si. (B) The permutation test of the OPLS-DA model. The x-axis represents the similarity, which is the proportion vs. Bad_Si. (B) The permutation test of the OPLS-DA model. The x-axis represents the similarity, which is the proportion consistent with the order of the Y variables of the original model. The y-axis represents Q2 and R2. consistent with the order of the Y variables of the original model. The y-axis represents Q2 and R2. 3.4. Identification of Differential Flavonoid Metabolites 3.4. IdentificationWe screened of Differen differentialtial flavonoidFlavonoid metabolites Metabolites between high and poor eating quality varieties by combining the fold-change (FC) and variable importance in project values (VIP). WeThere screened were 33 differential differential flavonoid flavonoid metabolites metabolit (19es up-regulated, between high 14 down-regulated) and poor eating quality varietiesincluding by combining 21 flavones, the six fold-change flavonols, two flavanones,(FC) and twovariable proanthocyanidins, importance and in twoproject values (VIP). otherThere flavonoids were 33 indifferential high-quality flavonoid varieties compared metabolites with poor (19 qualityup-regulated, varieties, with14 down-regulated) including|log2 FC| > 221 and flavones, VIP > 2 (Table six S3).flavonols, The volcano two plot flavanones, could broadly two display proanthocyanidins, the number of and differential flavonoids metabolites and the variation between high and poor eating quality two otherfoxtail flavonoids millet varieties in high-qua (Figure6A).lity These varieties metabolites compared might representwith poor the quality differential varieties, with |log2 FC|flavonoid > 2 and metabolites VIP > in2 (Table foxtail millet S3). varietiesThe volcano that caused plot different could broadly eating qualities. display the number of differentialWe conducted flavonoids HCA metabolites to compare the and differences the variation in 33 flavonoid between metabolites high amongand poor eating varieties. These metabolites were presented as a heatmap after normalization (Figure6B). qualityAs foxtail shown millet in the heatmap, varieties specific (Figure metabolites 6A). These in each metabolites variety were displayed might represent intuitively. the differential flavonoidAll of the 33 metabolites flavonoid metabolites in foxtail in the millet different varieties varieties that could caused be divided different into two eating cate- qualities. Wegories. conducted NMB and HCA DBQ were to clusteredcompare into the one differen category,ces whereas in 33 JG21, flavonoid QZH, and metabolites YG1 were among varieties.clustered These into metabolites the other, in whichwere JG21presented and QZH as shared a heatmap more similarities. after normalization Luteolin and its (Figure 6B). derivatives (pma6371, pmb0622, pmb0588), chrysoeriol O-malonylhexoside (pmb0608), and As showntricetin in O-malonylhexosidethe heatmap, specific (pma0795) metabolites were the metabolites in each variety more abundant were indisplayed three high- intuitively. All of qualitythe 33 varieties. flavonoid Syringetin metabolites 5-O-hexoside in the (pmb0569), different 8-C-hexosyl-hesperetin varieties could O-hexosidebe divided into two categories.(pmb0618), NMB procyanidin and DBQ A1were (pme0431), clustered and into syringetin one category, 7-O-hexoside whereas (pmb0602) JG21, were QZH, the and YG1 were clusteredmain differential into the flavonoid other, metabolites in which between JG21 and YG1 andQZH the shared other two more high-quality similarities. vari- Luteolin eties. Rutin (pme0197), showing the highest content in YG1 and lower in the other four and itsvarieties, derivatives was a derivative (pma6371, of quercetin pmb0622, and pertainedpmb0588), to flavonol. chrysoeriol Proanthocyanidins O-malonylhexoside (pmb0608),(pme0431, and pme0433) tricetin wereO-malonylhexoside mainly detected in NMB (pma0795) and DBQ. were the metabolites more abundant in three high-quality varieties. Syringetin 5-O-hexoside (pmb0569), 8-C-hexosyl-hesperetin O-hexoside (pmb0618), procyanidin A1 (pme0431), and syringetin 7-O-hexoside (pmb0602) were the main differential flavonoid metabolites between YG1 and the other two high-quality varieties. Rutin (pme0197), showing the highest content in YG1 and lower in the other four varieties, was a derivative of quercetin and pertained to flavonol. Proanthocyanidins (pme0431, pme0433) were mainly detected in NMB and DBQ.

Life 2021, 11Life, x2021 FOR, 11 PEER, 578 REVIEW 8 of 14 8 of 14

Figure 6.Figure Identification 6. Identification of differential of differential flavonoid flavonoid meta metabolitesbolites betweenbetween Good_Si Good_Si and and Bad_Si Bad_Si groups. groups. (A) The ( volcanoA) The plotvolcano of plot of differential flavonoid metabolites. Each point in the graph represents a metabolite. The red dots represent up-regulated differential flavonoid metabolites. Each point in the graph represents a metabolite. The red dots represent up-regulated metabolites, the green dots represent down-regulated metabolites, and the black dots represent metabolites with insignificant metabolites, the green dots represent down-regulated metabolites, and the black dots represent metabolites with insignif- differences. (B) Clustering heat map of differential flavonoid metabolites. Columns represented samples, and metabolites icant differences. (B) Clustering heat map of differential flavonoid metabolites. Columns represented samples, and me- were represented by rows. The data of metabolite content were normalized by the method of maximum difference tabolites normalization.were represented The up-regulated by rows. The and down-regulateddata of metabolite metabolites content were were represented normalized with red by and the green, method respectively. of maximum difference normalization. The up-regulated and down-regulated metabolites were represented with red and green, respectively. 3.5. Enrichment Analysis Clarified the Metabolic Pathways of Differential Flavonoid Metabolites 3.5. EnrichmentThe differential Analysis flavonoid Clarified metabolitesthe Metabolic were Pathways mapped of to D theifferential Kyoto EncyclopediaFlavonoid Metabolites ofThe Genes differential and Genomes flavonoid (KEGG) metabolites database (http://www.genome.jp/kegg/ were mapped to the Kyoto, accessed Encyclopedia on of 2019). We mainly identified two metabolic pathways, the flavonoid biosynthesis pathway Genes(ko00941) and Genomes and one of (KEGG) its branches, database flavone and(http: flavonol//www.genome.jp/kegg/, biosynthesis pathway (ko00944), accessed at on 2019). We amainly 0.05 level identified (p-value) (Figure two 7metabolic, Table S3). Thepathwa specificys, flavonoid the flavonoid metabolites biosynthesis in high eating pathway (ko00941)quality and varieties one were of its mainly branches, distributed flavone in the an flavonoidd flavonol biosynthesis biosynthesis pathway pathway (ko00941). (ko00944), at a In0.05 contrast, level specific(p-value) flavonoid (Figure metabolites 7, Table in S3). poor The eating specific quality varietiesflavonoid were metabolites distributed in high eatingprimarily quality on varieties the flavone were and flavonol mainly biosynthesis distributed pathway in the (ko00944). flavonoid biosynthesis pathway Based on the KEGG database, we constructed a foxtail millet flavonoid metabolic (ko00941).network In containing contrast, 33 specific differential flavonoid flavonoid metabolites metabolites, with in poor approximately eating quality two branches varieties were distributedfrom naringenin primarily (Figure on the8). Inflavone this network, and flavonol most enzymes biosynthesis might possess pathway functions (ko00944). in metabolite modification when most of the detected flavonoids were decorated forms by glycosyltransferase,Statistics hydroxylase, of KEGG and methyltransferase.Enrichment According to the network, flavonoid 30,50-hydroxylase (F3050H), flavone synthase I (FNSI), flavone synthasep-value II (FN- 0 0 1.00 0 FlavonoidSII), biosynthesis flavonoid 3 -monooxygenase● (CYP75B3), flavone 3 -O-methyltransferase (F3 OM), flavonoid 30,50-methyltransferase (AOMT), flavone 7-O-β-glucosyltransferase,0.75 flavonol 3-O-glucosyltransferase (F3OG), flavonol-3-O-glucoside L-rhamnosyltransferase0.50 (FG2) Flavone and flavonolbiosynthesisand quercetin 3-O-methyltransferase (OMT2) were the key enzymes● in the process0.25 of the differential metabolites biosynthesis. 0.00 0.30 0.35 0.40 Rich Factor

Figure 7. The KEGG classification of differential flavonoid metabolites. The x-axis indicates the Rich Factor, and the y-axis indicates the names of metabolic pathways. The color of the dot means p-value, the darker red, the greater degree of KEGG enrichment, and the size of the dot means the numbers of the differential metabolites.

Based on the KEGG database, we constructed a foxtail millet flavonoid metabolic network containing 33 differential flavonoid metabolites, with approximately two

Life 2021, 11, x FOR PEER REVIEW 8 of 14

Figure 6. Identification of differential flavonoid metabolites between Good_Si and Bad_Si groups. (A) The volcano plot of differential flavonoid metabolites. Each point in the graph represents a metabolite. The red dots represent up-regulated metabolites, the green dots represent down-regulated metabolites, and the black dots represent metabolites with insignificant differences. (B) Clustering heat map of differential flavonoid metabolites. Columns represented samples, and metabolites were represented by rows. The data of metabolite content were normalized by the method of maximum difference normalization. The up-regulated and down-regulated metabolites were represented with red and green, respectively.

3.5. Enrichment Analysis Clarified the Metabolic Pathways of Differential Flavonoid Metabolites The differential flavonoid metabolites were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/, accessed on 2019). We mainly identified two metabolic pathways, the flavonoid biosynthesis pathway (ko00941) and one of its branches, flavone and flavonol biosynthesis pathway (ko00944), at a 0.05 level (p-value) (Figure 7, Table S3). The specific flavonoid metabolites in high eating quality varieties were mainly distributed in the flavonoid biosynthesis pathway Life 2021, 11, 578 (ko00941). In contrast, specific flavonoid metabolites in poor eating quality varieties9 of 14 were distributed primarily on the flavone and flavonol biosynthesis pathway (ko00944).

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Statistics of KEGG Enrichment p-value 1.00 Flavonoid biosynthesis ● 0.75 branches from naringenin (Figure 8). In this network, most enzymes might possess func- 0.50 tions in metabolite modification when most of the detected flavonoids were decorated Flavone and flavonolbiosynthesisforms by glycosyltransferase, hydroxylase, and methyltransferase.● According0.25 to the network, flavonoid 3′,5′-hydroxylase (F3′5′H), flavone synthase Ⅰ (FNSⅠ), 0.00flavone synthase 0.30 0.35 0.40 Ⅱ (FNSⅡ), flavonoid 3‘-monooxygenaRich Factorse (CYP75B3), flavone 3′-O-methyltransferase (F3′OM), flavonoid 3′,5′-methyltransferase (AOMT), flavone 7-O-β-glucosyltransferase, Figure 7. The KEGG classiﬁcationflavonol of differential 3-O-glucosyltransferase ﬂavonoid metabolites. (F3OG), The x-axis flavonol-3-O-glucoside indicates the Rich Factor, L-rhamnosyltransferase and the y-axis Figure 7. The KEGG classification of differential flavonoid metabolites. The x-axis indicates the Rich Factor, and the y-axis indicates the names of metabolic(FG2) pathways. and Thequercetin color of 3-O-methyltransferase the dot means p-value, the(OMT2) darker were red, thethe greater key enzymes degree of in KEGG the process indicates the names of metabolic pathways. The color of the dot means p-value, the darker red, the greater degree of KEGG enrichment, and the size of the dotof the means differential the numbers metabolites of the differential biosynthesis. metabolites. enrichment, and the size of the dot means the numbers of the differential metabolites.

Based on the KEGG database, we constructed a foxtail millet flavonoid metabolic network containing 33 differential flavonoid metabolites, with approximately two

FigureFigure 8. The 8. pathwayThe pathway of differential of differential flavonoid flavonoid metabolites. metabolites. Blue Blue letters letters represent represent differential differential metabolites. metabolites. Orange Orange letters letters representrepresent enzymes enzymes in metabolic in metabolic pathways. pathways. A rectangle A rectangle with with a particular a particular color color represented represented the the relative relative content content of of each each metabolite. As shown in the upper-right bar, the up-regulated and down-regulated metabolites were represented with red metabolite. As shown in the upper-right bar, the up-regulated and down-regulated metabolites were represented with red and blue, respectively. CHS: chalcone synthase C2; CHI: chalcone isomerase; F3′5′H: flavanoid 3′,5′-hydroxylase; NOMT: and blue, respectively. CHS: chalcone synthase C2; CHI: chalcone isomerase; F3050H: flavanoid 30,50-hydroxylase; NOMT: naringenin 7-O-methyltransferase; CYP75B3: flavonoid 3′-monooxygenase; F3H: flavanone 3-dioxygenase; FNSI: flavone 0 naringeninsynthase 7-O-methyltransferase; I; FNSII: flavone synthase CYP75B3: II; FLS: flavonoid flavonol 3 synthase;-monooxygenase; FOM: flavone F3H: flavanone3′-O-methyltransferase; 3-dioxygenase; AOMT: FNSI: flavonoid flavone 0 synthase3′,5′-methyltransferase; I; FNSII: flavone synthase F3OG: flavonol II; FLS: flavonol3-O-glucosyltransferase; synthase; FOM: flavoneFG2: flavonol-3-O-glucoside 3 -O-methyltransferase; L-rhamnosyltransferase; AOMT: flavonoid 30,50-methyltransferase;OMT2: quercetin 3-O-methyltransferase. F3OG: flavonol 3-O-glucosyltransferase; FG2: flavonol-3-O-glucoside L-rhamnosyltransferase; OMT2: quercetin 3-O-methyltransferase. 4. Discussion 4. Discussion With high nutritional value, foxtail millet has received more and more attention from consumers.With high nutritionalAccording value,to the foxtail“Compendium millet has of received Materia moreMedica” and (a more book attention on traditional from consumers.Chinese medicine, According written to the “Compendiumin 1578), the millet of MateriaporridgeMedica” is exceedingly (a book nourishing on traditional for the Chinesestomach medicine, and effective written for in 1578),children’s the milletdiarrhea. porridge The yellow is exceedingly color is the nourishing primary indicator for the stomachfor consumers and effective to choose for children’s millet, and diarrhea. carotenoid Thes yellow and flavonoids color is the are primarythe dominating indicator yellow for consumerspigments to choosein dehulled millet, foxtail and carotenoidsmillet. Studies and have flavonoids shown arethat the the dominating accumulation yellow of carote- pig- mentsnoids in dehulled and flavonoids foxtail millet.contributes Studies to the have seeds’ shown palatability that the accumulation and nutritional of value carotenoids [36,37]. andAs flavonoids an important contributes provitamin to the A, seeds’carotenoids palatability can be andmetabolized nutritional to retinol, value [effectively36,37]. As anpre- importantventing provitamin vision loss A,[38]. carotenoids By comparing can bethe metabolized transcript levels to retinol, of carotenoid effectively structural preventing genes visionbetween loss [38 green]. By and comparing yellow color the transcript millets, the levels role ofof carotenoidcarotenoids structural in the formation genes between of yellow greenmillet and yellowcolor has color been millets, proven the [39]. role Flavonoids of carotenoids also in affect the formation the color, ofnutritional yellow millet value, color and hasantioxidant been proven properties [39]. Flavonoids of plants also and affect possess the color,a wide nutritional range of value,healthcare and activities antioxidant and propertiespharmacological of plants and functions possess [40]. a wide The rangestudies of on healthcare flavonoids activities in plants and have pharmacological also provided a

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functions [40]. The studies on flavonoids in plants have also provided a theoretical basis for its application in medicine, healthcare, food, and cosmetics [41]. Therefore, it is of great importance to explore the flavonoids in foxtail millet. Studies have shown the content of flavonoids varied among different species and different varieties of the same species [42,43]. It was reported that wheat varieties with different flavonoid content resulted in the difference in bread taste. Apigenin was only identified in the poor wheat variety [44], which was consistent with our findings. This could be applied to wheat breeding and improve wheat varieties using molecular breeding approaches. The flavonoid metabolites in different buckwheat varieties also provided a theoretical basis for the sufficient utilization of special buckwheat varieties [45]. Flavonoids in plant leaves play vital roles in defending against abiotic stresses [46]. Researchers have clarified the accumulation and variations of more than 300 metabolites in the leaves of foxtail millet at three-leaf and five-leaf stages [47]. Still, there is little research on the flavonoids in millet grains. In our study, flavonoid metabolites in foxtail millet varieties of different qualities were investigated. The three high eating quality varieties, JG21, QZH, and YG1 contained similar flavonoid contents. They were significantly different from NMB and DBQ, which provided a theoretical basis for the utilization of functional ingredients in foxtail millet. According to the comparison group, luteolin, quercetin, and their derivatives were mainly present in three high-eating quality varieties. As a flavone, luteolin is one of the most common flavonoids in edible plants, and it has been found in different fruits and vegetables, including carrots, peppers, and celery [48]. Recent research pointed out that luteolin might be a promising molecule for developing topic formulations and systemic agents against inflammatory skin diseases. It has also been suggested as a cancer chemopreventive agent [49,50]. In our study, luteolin was abundant in three high-quality foxtail millet varieties but absent in the other two varieties. Quercetin, belonging to flavonols, has been asserted to have many valuable effects on health, including preventing diseases such as lung cancer, osteoporosis, and inflammatory disorders [51,52]. It is a dietary flavonoid found in fruits (mainly citrus), many seeds (buckwheat), and so on [53]. Present studies have provided evidence that quercetin improves gut health and helps alleviate metabolic disorders, which is likely to be one reason why the millet porridge is effective for children’s diarrhea [54]. Most of the quercetin existed in YG1, and a small amount was detected in JG21. This study showed that the seven glycoside derivatives of chrysoeriol were mainly accumulated in JG21, QZH, YG1. The metabolites mentioned above might be part of the high eating quality of JG21, QZH, and YG1. Although exhibiting poor eating quality, specific flavonoids with pharmacological functions were also detected in NMB and DBQ. Procyanidins existed mainly in NMB, with YG1 and DBQ containing just a minute quantity of procyanidin A1. Previous studies have shown that procyanidins exert physiological and cellular activities, facilitate homeostasis, and possess anti-inflammatory effects in vitro and in vivo [55]. However, procyanidins gave the grape wine astringent taste [56,57], and this might also be a reason for the astringent flavor of NMB. Apigenin 7-O-glucoside (pmb0605) and di-C, C-hexosyl-apigenin (pmb0605) were glycoside compounds of apigenin, whereas the former mainly accumulated in NMB and the other mainly in DBQ. As a dietary flavonoid, apigenin is widely distributed in plants like celery, parsley, and chamomile, which has shown an interesting link between diet and treating chronic diseases, including cancer [58,59]. Foxtail millets with poor quality may also have a specific health role. Therefore, DBQ and NMB represent valuable resources for the improvement of foxtail millets. The flavonoid metabolic network we proposed here was based on the differential flavonoid metabolites. We specified the key enzymes that might regulate the biosynthesis of the differential flavonoid metabolites. However, the genes encoding these enzymes and their regulation mechanism of the whole flavonoid metabolic network need to be further studied. CitF3H in citrus plants was verified to convert naringenin into dihydrokaempferol, which contributed to the genetic improvement of citrus plants and the synthesis of ben- Life 2021, 11, 578 11 of 14

eficial flavonoids [43]. The functions of flavonoid-related enzyme genes in foxtail millet need to be verified. An enzyme (TraesCS1A01G347100) in wheat kernels has been shown to function on flavonoids (apigenin, kaempferol, naringenin) to produce various glycosylation products by in vitro enzymatic validation [60]. Derivatives of metabolites from different varieties also differed. For instance, the contents of four derivatives of chrysoeriol (pmb0701, pmb2976, pmb0607, pmb0608) were higher in high eating quality varieties, whereas pma6518 and pmb0600 were mainly found in two poor eating quality varieties, the enzymes that play catalytic roles in which need to be identified. In this flavonoid metabolic network, one gene may be involved in regulating the biosynthesis of multiple metabolites, or on the contrary, multiple genes may also be related to the same flavonoid. At present, a rapid-cycling mini foxtail millet mutant, xiaomi, has provided us with a good resource for deeper exploration of these genes [61]. Moreover, the complex structure of flavonoids poses a considerable challenge to their identification. Therefore, precise structure determination of the metabolites will help enlighten a complete network. Flavonoid metabolites varied greatly in the tested foxtail millet varieties, and each of them accumulated its unique metabolites, which might provide these varieties with potentially medical functions. Additionally, the result of this study identified the main biosynthesis pathways involved in these differential metabolites. Combing the whole regulation networks of flavonoids metabolism could provide a theoretical basis for researching the quality and the breeding for high-quality varieties in foxtail millet.

5. Conclusions In summary, we initially identified 116 flavonoid compounds in foxtail millet. Then we identified 33 differential flavonoid metabolites between high and poor eating quality varieties. By further analyzing these 33 metabolites, we found specific metabolites in each variety, such as the luteolin in three high eating quality varieties, the quercetin in YG1, and the procyanidins in NMB. Finally, we combined the differential metabolites with the KEGG database and found the key enzymes that might regulate the biosynthesis of these metabolites. This project provides information for the study of flavonoid biosynthesis in foxtail millet. It will also lay a theoretical foundation for the breeding of high-quality foxtail millet varieties.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/life11060578/s1, Table S1: Color parameter values of different foxtail millet varieties, Table S2: A list of the 116 flavonoid metabolites detected in the five foxtail millet varieties, Table S3: A list of the 33 differential flavonoid metabolites between high and poor eating quality varieties. Author Contributions: Conceptualization, Y.H. and X.L.; Methodology, X.L.; Software, X.L.; Formal Analysis, Y.Z. and X.L.; Resources, Y.H.; Data Curation, Y.Z. and Q.Q.; Writing—Original Draft Preparation, Y.Z.; Writing—Review & Editing, Y.H., J.G., and S.H.; Visualization, Y.Z. and Y.Y.; Supervision, X.W. and X.L.; Project Administration, J.G.; Funding Acquisition, Y.H., X.L., and S.H. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Scientific and Technological Innovation Programs of Shanxi Agricultural University (2017YJ27), the National Natural Science Foundation of China (NSFC) (32001608, 31371693), the National Key Research and Development Project (2018YFD1000705-2), the Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi (2019KJ020), the Youth Fund Project on Application of Basic Research Project of Shanxi Province (201901D211362), the Technological Innovation Programs of Higher Education Institutions in Shanxi (2019L0367), the Applied Basic Research Program in Shanxi (201801D221298) and the Innovation Programs of Graduate Education in Shanxi (2020SY208). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Life 2021, 11, 578 12 of 14

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