Targeted metabolomic and transcriptomic analyses provide insights into flavonoid biosynthesis in the grain of foxtail millet

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Despite the nutritional significance of flavonoids, their biosynthetic pathways in foxtail millet remain poorly characterized. In this study, we integrated targeted metabolomic and transcriptomic analyses to systematically elucidate the flavonoid biosynthesis pathway and identify genes encoding key regulators. Results Quantitative profiling of a foxtail millet recombinant inbred line (RIL) population revealed differences in the grain flavonoid content, with flavonoid levels 5-fold higher in the high-flavonoid (HF) group than in the low-flavonoid (LF) group. Targeted metabolomic and transcriptomic analyses revealed key regulatory networks controlling flavonoid biosynthesis in foxtail millet. A comparative transcriptomic analysis detected significant differences in the expression profiles of flavonoid biosynthesis-related genes between the HF and LF groups. According to a targeted metabolomic analysis, the concentrations of 10 distinct flavonoids were significantly higher in the HF group than in the LF group. Integrated analyses indicated that genes encoding shikimate O-hydroxycinnamoyl transferase ( HCT ), phenylalanine ammonia-lyase ( PAL ), and phenylalanine/tyrosine ammonia-lyase ( PTAL ) are crucial for the observed differences in the flavonoid contents of the HF and LF groups. Conclusions These findings provide insights into the genetic regulation of flavonoid metabolism in foxtail millet. Furthermore, this study identified candidate genes that may be useful molecular targets for breeding foxtail millet varieties with optimal nutritional quality. Foxtail millet Targeted metabolomics Transcriptomics Flavonoid biosynthesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Foxtail millet ( Setaria italica L.), a nutrient-rich cereal domesticated from its wild progenitor ( Setaria viridis ) in northern China more than 8,000 years ago, has emerged as a promising functional food crop because of its natural antioxidant properties [ 1 , 2 ]. In terms of nutritional components, this drought-tolerant species is similar to major cereals, such as wheat and rice, making it an important source of protein, dietary fiber, and essential minerals [ 3 , 4 ]. In addition, foxtail millet accumulates diverse phytochemicals, including flavonoids, phenolic acids, and phytosterols, which collectively contribute to its health-promoting effects [5]. Flavonoids, a major class of plant secondary metabolites, are structurally diverse compounds with varying hydroxylation patterns, oxidation states, and substituent positions, which have been used to divide them into distinct subclasses: anthocyanins, flavanols, flavanones, flavones, flavonols, and isoflavones [ 6 ]. These phytochemicals, including flavonoids and phenolic acids, have dual roles as chromatic determinants responsible for grain pigmentation (ranging from white to black) and bioactive agents with antioxidant and anti-inflammatory properties [ 7 ]. Flavonoids exhibit antioxidant, anti-inflammatory, anticancer, cardiovascular protective, and antiviral properties [ 8 – 11 ], and can be used to treat inflammation-related conditions (e.g., arthritis and sore throat). Their anticancer properties reportedly can enhance immunity [ 12 – 13 ]. To date, 116 flavonoids have been identified in foxtail millet, including catechins, rutin, vitexin, luteolin, kaempferol, and quercetin [ 14 ]. Flavonoid biosynthetic pathways have been thoroughly studied in various plants, including Malus pumila Mill. [ 15 ], Salvia miltiorrhiza [16], Arabidopsis thaliana , and Zea mays [ 17 ]. In contrast to the extensive genomic and metabolomic studies involving major crops ( Oryza sativa and Z. mays ) and model organisms ( A. thaliana ), there has been limited research on foxtail millet, an increasingly agronomically important drought-tolerant cereal, resulting in fragmented findings and insufficient systemic integration. Previous research on foxtail millet focused on determining nutrient contents, analyzing gene expression via RNA-sequencing (RNA-seq), and cloning and validating functional genes [ 18 – 20 ]. Several recent studies combined transcriptomic and metabolomic analyses to explore regulatory mechanisms related to certain functional components. Such studies have been conducted for honeysuckle [ 21 ], cucumber [ 22 ], Dendrobium species [ 23 ], and peanut [ 24 ]. He et al. [ 25 ] reported that carotenoid cleavage dioxygenase helps degrade lutein, affecting carotenoid accumulation and color development in foxtail millet grains. In the current study, transcriptomic and metabolomic analyses were conducted to investigate flavonoid biosynthesis in foxtail millet grains. The study findings provide critical genetic resources and molecular markers relevant to breeding nutrient-rich foxtail millet cultivars, thereby enhancing agricultural profitability and socioeconomic sustainability in millet-producing regions. Results Analysis of flavonoid content variations in a recombinant inbred line (RIL) population We determined the total flavonoid content in mature grains obtained from a population comprising 180 foxtail millet RILs. The generated dataset revealed a continuous phenotypic variation in flavonoid accumulation, conforming to a normal distribution (Fig. 1A). Accordingly, the foxtail millet grain flavonoid content is a quantitative trait influenced by the environment. We hypothesized that multiple genes co-regulate this trait. Because of the complexity of quantitative traits in cereals and the difficulty in localizing related genes, we constructed an RIL population and then conducted targeted metabolomic and transcriptomic analyses to map foxtail millet flavonoid biosynthesis-related genes. The RILs were ranked on the basis of the grain flavonoid content (from highest to lowest). The 30 lines with the highest flavonoid contents and the 30 lines with the lowest flavonoid contents were selected as a mixed pool. The flavonoid content was approximately 5-fold higher in the high-flavonoid (HF) RIL group than in the low-flavonoid (LF) RIL group (Fig. 1B). Identification of metabolites Metabolomic profiles were compared between the HF and LF foxtail millet groups. On the basis of a principal component analysis (PCA), PC1 and PC2 explained 69.30% and 21.50% of the variance in grain metabolomic profiles, respectively (Fig. 2 A). An orthogonal partial least squares discriminant analysis (OPLS-DA) of the foxtail millet grain metabolite data resulted in R2Y and Q2 values of 0.940 and 0.997, respectively (Fig. 2 B), which reflected the stability and reliability of the OPLS-DA model. PCA and OPLS-DA score plots revealed the significant separation between the LF and HF groups, indicative of the differences in metabolism between the two groups. Identification of differentially accumulated flavonoid metabolites A total of 40 flavonoid metabolites were detected in the LF and HF groups (three biological replicates per line). Supplementary Table S1 provides detailed information about the detected flavonoid metabolites, including their molecular weight, formula, class, and Kyoto Encyclopedia of Genes and Genomes (KEGG) ID. Flavonoids (32.5%), acids (22.5%), flavanols (12.5%), flavonols (10%), dihydroisoflavones (7.50%), and chalcones (7.50%) were the main flavonoid metabolites (Fig. 3 A). After pre-treatments, such as filtering individual metabolites and simulating missing values in the original data, 36 differentially accumulated metabolites (DAMs) were detected between the HF and LF groups. To functionally characterize flavonoid DAMs and identify their associated metabolic pathways, a KEGG pathway enrichment analysis was conducted, which revealed the flavonoid pathway was significantly enriched among the DAMs in the HF and LF groups (Fig. 3 B). These results further confirmed the substantial differences in flavonoid metabolism between the HF and LF foxtail millet groups. A comparison between the HF and LF groups indicated that among the identified DAMs, 10 were up-regulated and 26 were down-regulated in the HF group. Flavonoid DAMs accounted for most of the up-regulated metabolites in the LF group (Supplementary Table S2). Moreover, flavonoid DAMs, including vanillin, syringaldehyde, (+)-dihydrokaempferol, naringenin chalcone, naringenin, apigenin, protocatechualdehyde, and trans-ferulic acid, were the main up-regulated DAMs (approximately 50%) revealed by the HF vs LF comparison. Transcriptome analysis An RNA-seq analysis was performed to examine the gene expression profiles of HF and LF foxtail millet grains. The raw data were deposited in the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov/sra/PRJNA1183448). The RNA-seq analysis generated 42.81 Gb clean data, with a GC content exceeding 57.3% for each sample. The Phred value at Q30 ranged from 91.58–92.95% (Table 1). Table 1 Basic details regarding RNA-seq data Sample Clean Base(bp) GC content(%) Q20(%) Q30(%) LF-1 7.61G 57.8 97.2 92.85 LF-2 6.47G 57.17 96.61 91.58 LF-3 7.14G 57.6 97.25 92.88 HF-1 7.79G 57.13 97.26 92.95 HF-2 6.9G 57.4 97.16 92.71 HF-3 6.9G 57.22 97.13 92.71 According to a comparison with the LF group (control), 582 genes were differentially expressed in the HF group (146 up-regulated genes and 436 down-regulated genes) (Fig. 4). The criteria for identifying differentially expressed genes (DEGs) were as follows: |log 2 (fold-change)| > 1.0 and P < 0.05. Gene Ontology (GO) and KEGG analyses were performed to further clarify DEG functions and their related biological processes. On the basis of a GO analysis, DEGs were annotated with 399 GO terms from the three main categories (molecular function, cellular component, and biological process). The enriched GO terms assigned to DEGs in grains included the following: “movement of cell or subcellular component” (GO:0006928, nine DEGs), “microtubule-based movement” (GO:0007018, nine DEGs), and “chromosome organization” (GO:0051276, eight DEGs) from the biological process category; “nutrient reservoir activity” (GO:0045735, seven DEGs), “protein heterodimerization activity” (GO:0046982, 11 DEGs), “microtubule motor activity” (GO:0003777, nine DEGs), and “microtubule binding” (GO:0008017, nine DEGs) from the molecular function category; and “chromosome” (GO:0005694, three DEGs), “chromosomal part” (GO:0044427, two DEGs), and “extracellular region” (GO:0005576, two DEGs) from the cellular component category (Supplementary Fig. S1). Hence, the DEGs were predominantly associated with subcellular component motility and microtubule dynamics regulation, which is in accordance with the transport mechanisms and antioxidant properties of flavonoid compounds in plants. A KEGG pathway enrichment analysis of the DEGs was conducted [26]; the top 20 enriched KEGG pathways among the DEGs in the HF and LF groups are presented in Fig. 5. Notably, four and two DEGs were involved in phenylpropanoid biosynthesis and phenylalanine metabolism, respectively. Among the DEGs involved in the phenylpropanoid biosynthesis pathway, two PAL genes (phenylalanine ammonia-lyase) and SPC4 (cationic peroxidase) were up-regulated in the HF group, whereas POD (peroxidase), ACT (agmatine coumaroyltransferase), and Met (methyltransferase) were down-regulated in the HF group (Table 2). Table 2 Enrichment of DEGs in the KEGG pathway related to flavonoid biosynthesis Expression patern Gene ID Gene name Descrition Log 2 (FoldChange) Up regulated Seita.1G240600 LOC101781925 Phenylalanine ammonia-lyase ( PAL ) 2.96087934 Seita.5G462500 LOC111256430 Cationic peroxidase SPC4 (CAT) 3.161535713 Seita.6G181000 LOC101766005) Phenylalanine tyrosine/ammonia-lyase ( PTAL ) 1.461248461 Down regulated Seita.2G431300 LOC111256430 Peroxidase 70 ( POD ) -1.935260709 Seita.8G188600 LOC101758480 Agmatine coumaroyltransferase-2 -5.291043079 Seita.8G210100 LOC101772386 Probable inactive methyltransferase Os04g0175900 -2.516508672 Verification of RNA-seq data To verify the reliability of the RNA-seq data, we selected eight DEGs and determined their transcript levels in the HF and LF groups via a quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The consistency between the DEG expression data generated by qRT-PCR and the RNA-seq data (Fig. 6) suggested that the RNA-seq data were reliable and applicable for a molecular analysis of flavonoid biosynthesis and gene mining. Correlation analysis of metabolomic and transcriptomic data Correlations between DAMs and DEGs were assessed on the basis of a combined analysis of metabolomic and transcriptomic data. We constructed a nine-quadrant diagram to visualize the relationships between DAMs and their associated genes (Spearman’s rank correlation coefficient ≥ 0.8). Most of the DAMs and DEGs in foxtail millet grains were clustered in quadrant 1 (those with positive correlations) and quadrant 7 (those with negative correlations) (Fig. 7 ). To further characterize flavonoid biosynthetic pathways in foxtail millet grains, we conducted pathway and regulatory network analyses of DAMs and DEGs related to phenylpropanoid biosynthesis and phenylalanine metabolism (Fig. 8). The following three genes, which are the main genes involved in flavonoid biosynthesis, were highly correlated (r > 0.8 or r < − 0.8) with 15 DAMs (Supplementary Table S3): Seita.8G188600 ( HCT ), Seita.1G240600 ( PAL ), and Seita.6G181000 ( PTAL ). Similar correlation analyses of DEGs and DAMs associated with flavonoid biosynthesis and phenylalanine metabolism indicated that Seita.8G188600 ( HCT ) was highly correlated (r > 0.8 or r 0.8 or r < − 0.8) with six flavonoids related to phenylalanine metabolism (Fig. 8 and Supplementary Table S3). PAL activity governs changes in phenylpropanoid metabolism. The resulting cinnamic acid is subsequently hydroxylated to p-coumaric acid, which is then converted to p-coumaroyl-CoA (i.e., essential precursor for flavonoid biosynthesis). Flavonoid biosynthesis-related gene expression A qRT-PCR analysis was completed to verify the relative expression of three genes encoding key enzymes involved in flavonoid biosynthesis in grains (Fig. 9 ). The results showed that PAL ( Seita.1G240600 ) and PTAL ( Seita.6G181000 ) expression levels were significantly up-regulated in the HF group, whereas HCT ( Seita.8G188600 ) expression was up-regulated in the LF group. These findings indicate that the expression of flavonoid biosynthesis-related genes, especially PAL , is induced to varying degrees in foxtail millet grains. Discussion Previous studies on critical metabolic genes in plants involved metabolomic and transcriptomic analyses of different organs [ 27 – 32 ]. Flavonoids are major nutrients in crops and regulate several important biological processes. A total of 14 transcription factor-encoding genes and 17 flavonoid biosynthesis-related genes have been identified in the peanut testa [ 33 ]. However, very little is known about flavonoid compositions and the molecular mechanisms regulating flavonoid biosynthesis in foxtail millet grains. According to earlier research, the total flavonoid content of red-grain millet is much higher than that of yellow-grain millet [ 34 ]. In the current study, the total flavonoid content was 5-fold higher in the HF group than in the LF group (Fig. 1B). Candidate genes involved in the accumulation of flavonoids in grains were preliminarily identified following a comparison between the HF and LF groups. Transcriptome libraries were constructed for HF and LF foxtail millet grains for the subsequent high-throughput sequencing analysis conducted to identify nutrients and their biosynthetic mechanisms. Moreover, flavonoid metabolites in foxtail millet grains were identified using an ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) system. After sequences were assembled, 582 DEGs in the HF and LF groups were identified. Additionally, 36 flavonoid metabolites in foxtail millet grains were identified using metabolomics techniques. The generated transcriptomic and metabolomic data provide an important basis for future explorations of metabolic pathways in foxtail millet. Flavonoids are one of the major nutritional components of foxtail millet grains. According to a KEGG analysis, flavonoid metabolites that accumulate differentially in foxtail millet grains include flavonoids, phenylpropanoids, phenylalanine, dihydroflavone, isoflavones, and flavonoid carbonoside anthocyanins; the associated biosynthetic pathways were among the significantly enriched KEGG pathways. This is crucial information for elucidating the molecular mechanisms regulating flavonoid accumulation in foxtail millet grains. A metabolomic analysis of foxtail millet grains detected 10 flavonoid metabolites that were more abundant in the HF group than in the LF group, including naringenin, naringenin chalcone, apigenin, vanillin, syringaldehyde, trans-ferulic acid, and phthalic acid. Naringenin was recently revealed to have numerous biological effects (e.g., antioxidant, anti-inflammatory, and metabolic regulatory effects). Several flavonoids isolated from the peanut testa, such as cyanidin, peonidin, and pelargonidin, reportedly have significant anti-inflammatory, immune-enhancing, and anticancer properties [ 35 ]. Apigenin has some medicinal properties. More specifically, it can decrease the risk of cancer [ 36 ], has neuroprotective properties [ 37 ], and lowers blood glucose and lipid levels [ 38 ]. The consumption of foxtail millet grains has beneficial effects on health. However, our analysis of the metabolites in foxtail millet grains demonstrated that metabolites related to flavonoid biosynthesis accumulate differentially in the HF and LF groups, suggesting that appropriately modifying different grain groups may lead to improved flavonoid use and efficacy. These findings are relevant to future evaluations of crop plants, with potential implications for the applicability of different foxtail millet groups. A lack of genetic data has delayed research on metabolic pathways in foxtail millet. To further elucidate the regulation of flavonoid accumulation in different foxtail millet groups, DEGs in HF and LF grains identified following an RNA-seq analysis were subjected to a KEGG pathway enrichment analysis. The results revealed the enrichment of flavonoid biosynthesis, flavone and flavonol biosynthesis, isoflavonoid biosynthesis, and upstream phenylpropanoid biosynthesis pathways in all samples. The basic flavonoid metabolic pathways in plants have been characterized, with various genes (e.g., CHS , CHI , CYP75A , ANR , FLS , and DFR ) encoding proteins with key roles in the synthesis of bioactive components in plants [ 39 – 47 ]. In the present study, PAL and PTAL expression levels were higher in HF grains than in LF grains (Fig. 9 ), which led to the increased accumulation of phenylalanine ammonia-lyase. The results of the UPLC-ESI-MS/MS-based analysis were also confirmed (Supplementary Table 3). Phenylalanine ammonia-lyase is a key enzyme in the flavonoid biosynthesis pathway, but the enzymes encoded by different PAL genes may have the same or different functions [ 48 ]. In the current study, PAL transcript levels were positively correlated with apigenin, vanillin, and naringenin contents, but PTAL transcript levels were negatively correlated with salicylic acid and catechin contents (Supplementary Table S3). In A. thaliana , PAL and PTAL genes encode proteins with important functions related to flavonoid biosynthesis [ 49 ]. The main flavonoids that accumulated more in the HF group than in the LF group were naringenin and aldehyde derivatives, such as naringenin chalcone, vanillin, and syringaldehyde, suggesting that PAL catalyzes the conversion of chalcone to naringenin. Other substrates that are specifically bound by PAL are ferulic acid, vanillic acid, and syringic acid [ 50 ]. HCT , which belongs to the plant acyltransferase family, catalyzes reactions involving various substrates, such as quercetin, kaempferol, quinic acid, and gentianic acid, leading to the formation of ester or amide compounds. It has a central role in the biosynthesis of chlorogenic acid and lignin [ 51 – 53 ]. The roles of HCT in lignin biosynthesis were recently validated, while its involvement in the synthesis of chlorogenic acid and flavonoids has also been determined. In A. thaliana , inhibited AtHCT expression is accompanied by a decrease in lignin accumulation, but an increase in flavonoid levels [ 54 ]. In foxtail millet, HCT catalyzes the conversion of p-coumaroyl-CoA to caffeoyl-CoA, which is subsequently converted to lignin precursors by a transferase. In the present study, HCT expression was significantly down-regulated in the HF group, thereby disrupting the synthesis and accumulation of lignin precursors. This, in turn, promoted the synthesis and accumulation of flavonoids via an alternative metabolomic pathway, with naringenin chalcone production being significantly up-regulated. The results of the transcriptomic and targeted metabolomic analyses performed in this study provide new information about flavonoid biosynthesis in cereals. However, additional research is required to comprehensively clarify the specific mechanisms regulating flavonoid compositions and contents in foxtail millet. In general, flavonoid synthesis in foxtail millet involves dynamic interactions among multiple regulatory networks. Ultimately, flavonoid compositions and contents in foxtail millet grains depend on the coordinated effects of the overall regulatory network. Conclusion The total flavonoid content in foxtail millet grains was significantly higher in the HF group than in the LF group. Comparisons of the transcriptomic and targeted metabolomic profiles of the HF and LF foxtail millet groups revealed the factors influencing flavonoid accumulation and their related candidate genes. Notably, PAL , PTAL , and HCT were identified as genes encoding key regulators of flavonoid biosynthesis in foxtail millet. Both PAL and PTAL positively regulate flavonoid accumulation, while HCT negatively regulates flavonoid synthesis and accumulation. These study results provide new insights relevant to breeding and selecting foxtail millet lines with improved nutrient profiles. Materials and methods Plant materials and sample collection The RIL population included in this study was derived from a cross between the HF variety ‘Jinmiaohongjiugu’ (maternal parent) and the LF variety ‘Yugu28’ (paternal parent). The RIL population consisted of 325 lines, with 180 lines selected for the screening of HF and LF lines based on the grain flavonoid content at maturity. The 30 lines with the highest grain flavonoid contents were included in the HF group, whereas the 30 lines with the lowest grain flavonoid contents were included in the LF group. Three biological replicates of mature grain samples were collected and immediately frozen in liquid nitrogen before being stored at − 80°C until the subsequent transcriptomic and metabolomic analyses. Determination of grain flavonoid contents Grain flavonoid contents were determined for the 180 selected RILs using a plant flavonoid content determination kit (Beijing Solarbio Science & Technology Co., Ltd., Beijing, China). Millet grain samples were dried and pulverized and then passed through a 50-mesh sieve. Approximately 0.1 g powdered sample was added to 1 mL extraction solution, after which the mixture was sonicated (300 W for 30 min at 60°C). The mixture was centrifuged (12,000 rpm for 10 min at 25°C) and the supernatant was retained. Test reagents were mixed thoroughly and then incubated in a water bath (37°C) for 45 min. The mixture was centrifuged (1,000 rpm for 10 min at room temperature) and the supernatant was retained. The absorbance of the supernatant was measured at 470 nm, after which a rutin standard curve was used to determine the flavonoid content. This analysis was completed three times per sample. The rutin standard curve was prepared by diluting a rutin standard solution (10 mg/mL) to 1.5, 1.25, 0.625, 0.3125, 0.15625, 0.078, 0.039, and 0.02 mg/mL using a standard diluent. SPSS software was used to analyze the grain flavonoid contents of 180 RILs, whereas Excel software was used to select 30 lines with low grain flavonoid contents and 30 lines with high grain flavonoid contents to construct a mixed pool. Targeted metabolomic profiling and analysis Sample preparation and extraction: Frozen plant samples (100 mg each) were sonicated for 30 min using 500 µL 80% (v/v) methanol containing 0.2% (w/v) vitamin C. The mixture was centrifuged (12,000 rpm for 10 min) and the supernatant was retained. These steps were repeated twice and the resulting supernatants were combined. UPLC conditions: Sample extracts were analyzed using a UPLC-Orbitrap-MS system (UPLC, Vanquish; MS, QE; Thermo Fisher Scientific, Waltham, MA, USA). Analytical conditions were as follows: UPLC: column, Waters HSS T3 (50 × 2.1 mm, 1.8 µm); column temperature, 40°C; flow rate, 0.3 mL/min; injection volume, 2 µL; solvent system, water (0.1% v/v acetic acid):acetonitrile (0.1% v/v acetic acid); gradient program, 90:10 (v/v) at 0 min, 90:10 (v/v) at 2.0 min, 40:60 (v/v) at 6.0 min, 40:60 (v/v) at 8.0 min, 90:10 (v/v) at 8.1 min, and 90:10 (v/v) at 12.0 min [ 55 ]. LC-MS/MS analysis: HRMS data were recorded using a Q Exactive hybrid Q-Orbitrap mass spectrometer equipped with a heated ESI source (Thermo Fisher Scientific) according to fullms-ms2 acquisition methods. ESI source parameters were as follows: sheath gas, 40 arb; auxiliary gas, 10 arb; spray voltage, − 2,800 V; temperature, 350°C; and ion transfer tube temperature, 320°C. The single ion monitor mode (negative ion mode) was applied [ 56 ]. Public metabolite databases (e.g., MassBank, KNAPSAcK, HMDB, and METLIN) and the MVDB v2.0 database from Smart Biotechnology Co., Ltd. (Tianjin, China) were used for the qualitative analysis of primary and secondary mass spectrometry data. Accordingly, structural characteristics of the metabolites were determined. For the quantitative analysis of metabolites, MRM was used and PCA and OPLS-DA were performed to identify DAMs. We divided foxtail millet lines into LF and HF groups for a comparative analysis. The following thresholds were used to identify significant DAMs: VIP > 1 and |log 2 (HF group/LF group)| ≥ 1. RNA isolation and transcriptomic analysis Total RNA was isolated and purified from foxtail millet grains using a Complex Plant RNA Rapid Extraction Kit (Beijing Labhelper Biotechnology Co., Ltd., Beijing, China). Samples were treated with an RNase-free DNase I digestion kit (Aidlab, Beijing, China) to remove genomic DNA contaminants. RNA degradation was assessed using 1% agarose gels, while RNA concentrations were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). After verifying the quality of the constructed libraries using a 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA), they were sequenced using an Illumina HiSeq™ 2000 system (Illumina, San Diego, CA, USA). Raw data were filtered by removing reads with adapter sequences and low-quality reads using fastp software [ 57 ]. The remaining clean reads were assembled to obtain unigenes, which were functionally annotated on the basis of NCBI non-redundant protein sequences (Nr; https://fp.ncbi.nlm.nih.gov/blast/db/FASTA/ ) as well as sequences in the KEGG ( https://www.genome.jp/kegg ), Clusters of Orthologous Groups of proteins (COG; https://www.ncbi.nlm.nih.gov/COG/ ), GO ( https://www.geneontology.org ), Swiss-Prot ( http://www.ebi.ac.uk/uniprot/ ), and translation of EMBL (TrEMBL) databases. Similarly, we conducted a comparative analysis of the HF and LF groups. DEGs were screened using the following criteria: |log 2 (fold-change)| > 1 and P < 0.05. DEG biological functions and associated metabolic pathways were determined using GO ( http://geneontology.org/ ) and KEGG ( http://www.genome.jp/kegg/ ) databases [ 58 – 60 ]. Integrated analysis of metabolomic and transcriptomic data Transcriptomic and metabolomic data for the grains in the HF and LF foxtail millet groups were analyzed. DEGs related to flavonoid biosynthesis and flavonoid DAMs in each comparison group (three replicates each) were subjected to a correlation analysis. Spearman’s correlation coefficients were used to determine differences between flavonoid biosynthesis-related genes and transcription factor genes (correlation coefficient ≥ 0.90 and P ≤ 0.05). qRT-PCR analysis On the basis of metabolomic, transcriptomic, and combined analyses, eight candidate DEGs identified by the RNA-seq analysis were selected for a qRT-PCR analysis. RNA was reverse transcribed to cDNA using a PrimeScript™ RT reagent kit (Tolo Biotech, Shanghai, China). For the qRT-PCR analysis, 2× Q3 SYBR qPCR Master Mix (Tolo Biotech) and a CFX96 Real-Time PCR Detection System (Bio-Rad, USA) were used. Gene-specific qRT-PCR primers (Supplementary Table S4) were designed using Beacon Designer 8.0. The qRT-PCR conditions were as follows: pre-denaturation at 95°C for 30 s; 40 cycles of 95°C for 30 s, 59°C for 30 s, and 72°C for 30 s. Relative gene expression levels were calculated according to the 2 −ΔΔCT method, with three biological replicates per gene [ 61 ]. Statistical analysis Transcriptomic and metabolomic analyses were completed using three biological replicates. Statistical analyses were performed using SPSS 22.0 software (IBM, Chicago, IL, USA). An independent samples t -test was used for the statistical comparison of HF and LF groups. Declarations Additional files Supplementary Table 1 Details regarding the identified metabolites in the grains of the LF and HF foxtail millet groups; Supplementary Table 2 DAMs between low-flavonoid and high-flavonoid foxtail millet grains; Supplementary Table 3 Correlations between DEGs encoding enzymes related to flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism and DAMs in grains; Supplementary Table 4 Details regarding qRT-PCR primers; Supplementary Fig. S1 GO classification of unigenes. Author contributions N.Q. and Z.Y. are co-first authors with equal contributions, and conceived and designed the research. S.F., C.Z., Y.J. acquired the data. N.Q., Z.Y., H.Z. and S.D. analyzed the data. N.Q., Z.Y., and L.J. drafted and revised the manuscript. L. J., C.W. and X.W. extracted and analyzed samples. All authors agreed to publish the manuscript. Funding This study was supported by the Scientific Research Plan Joint Fund of Henan Province (232301420105), China Agricultural Research System of the Ministry of Finance and Ministry of Agriculture and Rural Affairs (CARS-06), the Key Research and Development Projects of Henan Province (231111110300, 241111112100), the Funding of Joint Research on Agriculture Varieties of Henan Province (2022010401), and Innovation team of Henan Academy of Agricultural Sciences (2024TD39). Availability of data and materials The datasets supporting the conclusions of this article has been deposited in the National Center for Biotechnology Information DataBase (NCBIdb, https://www.ncbi.nlm.nih.gov/sra/PRJNA1183448) under the accession number PRJNA1183448. Acknowledgments We thank Xianmin Diao from China Academy of Agriculture Sciences for improving the article’s data analysis. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details 1 Cereal Crop Research Institute, Henan Academy of Agricultural Sciences, Postgraduate T&R Base of Zhengzhou University, Zhengzhou 450002, China. 2 School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China. 3 Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China. 4 Nanyang Academy of Sciences, Nanyang 473000, China. References Wei W, Li S, Wang, Y, et al. Metabolome-Based Genome-Wide Association Study Provides Genetic Insights Into the Natural Variation of Foxtail Millet. Front Plant Sci, 2021, 12: 665530. Yang X, Wan Z, Perry L, et al. Early millet use in northern China. Proc Natl Acad Sci, 2012, 109: 3726−3730. Zhang M, Xu Y, Xiang J, et al. Comparative evaluation on phenolic profiles, antioxidant properties and ɑ-glucosidase inhibitory effects of different milling fractions of foxtail millet. Cereal Sci, 2021, 99: 103217. 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Supplementary Files Supplementarydocument.zip Supplementary Table 1 Details regarding the identified metabolites in the grains of the LF and HF foxtail millet groups; Supplementary Table 2 DAMs between low-flavonoid and high-flavonoid foxtail millet grains; Supplementary Table 3 Correlations between DEGs encoding enzymes related to flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism and DAMs in grains; Supplementary Table 4 Details regarding qRT-PCR primers; Supplementary Fig. S1 GO classification of unigenes. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 28 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviews received at journal 12 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 05 Apr, 2025 Submission checks completed at journal 05 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5534545","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439189149,"identity":"e250483e-2b9f-4d4e-b043-d4ece6978112","order_by":0,"name":"Zhenyan Ye","email":"","orcid":"","institution":"Henan Academy of Agricultural Sciences, Postgraduate T\u0026R Base of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhenyan","middleName":"","lastName":"Ye","suffix":""},{"id":439189150,"identity":"d4b37f21-e5d5-4ace-ae3f-e197c103e5ab","order_by":1,"name":"Na Qin","email":"","orcid":"","institution":"Henan Academy 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10:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5534545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5534545/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11780-x","type":"published","date":"2025-07-01T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80247758,"identity":"781265cc-f5ab-4224-b981-89fa95b31054","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93437,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation flavonoid content. (A) Frequency distribution of the grain flavonoid content in the recombinant inbred line (RIL) population. (B) Grain flavonoid contents in the high-flavonoid (HF) and low-flavonoid (LF) groups. Data are presented as the mean ± SD (n = 30). Asterisks indicate statistically significant differences according to Student’s \u003cem\u003et\u003c/em\u003e-test (P \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/b9f3732e733815f942645c92.jpg"},{"id":80248533,"identity":"a672c417-549c-4c8c-8698-f0d9dc438f07","added_by":"auto","created_at":"2025-04-09 16:24:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66411,"visible":true,"origin":"","legend":"\u003cp\u003ePCA and OPLS-DA score plots of foxtail millet grain metabolite profiles. (A) PCA score plot of LF and HF samples; Principal component 1 (PC1) and principal component 2 (PC2) are presented on the x- and y-axes, respectively. (B) Permutation test for OPLS-DA of LF \u003cem\u003evs\u003c/em\u003e HF. LF, low-flavonoid lines; HF, high-flavonoid lines.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/b1c4ac500552670afe2c9004.jpg"},{"id":80247763,"identity":"2de5f362-600e-40e9-a206-438a5fd2961f","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137432,"visible":true,"origin":"","legend":"\u003cp\u003ePutatively annotated metabolites and enriched pathways among differentially accumulated metabolites (DAMs). (A) Putatively annotated metabolites in the LF and HF groups. (B) Classes of DAMs in the LF and HF groups and their associated KEGG pathways. LF, low-flavonoid lines; HF, high-flavonoid lines.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/bbbf9ac0f4b477307abba0db.jpg"},{"id":80247762,"identity":"722323b1-f64a-44be-a639-80149a497bee","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52210,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of up- and down-regulated differentially expressed genes in the HF and LF groups. LF, low-flavonoid lines; HF, high-flavonoid lines.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/e0c55259e79b05c8c7c487e5.jpg"},{"id":80247768,"identity":"08d3f8e8-81ce-43ae-b776-fd329353ae0c","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110022,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway classification of differentially expressed genes in high-flavonoid and low-flavonoid foxtail millet grains.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/f8ef660873568af92d31419f.jpg"},{"id":80247771,"identity":"df2d2784-5c04-4798-a534-9f3c44077b25","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":117881,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the RNA-seq data of eight DEGs in foxtail millet via qRT-PCR. Data are presented as the mean ± SD of three biological replicates. LF, low-flavonoid lines; HF, high-flavonoid lines.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/753032e74d49ca23a06f595a.jpg"},{"id":80247765,"identity":"cdf074ea-5da8-4b81-8a85-08800a844e0b","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":37736,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between differentially accumulated metabolites (DAMs) and differentially expressed genes (DEGs) in high-flavonoid (HF) and low-flavonoid (LF) foxtail millet grains. The x-axis presents the log\u003csub\u003e2\u003c/sub\u003e ratio of metabolite abundance, whereas the y-axis presents the log\u003csub\u003e2\u003c/sub\u003e ratio of transcript abundance. The dashed line on the horizontal coordinate represents the fold difference threshold for metabolites, whereas the dashed line on the vertical coordinate represents the fold difference threshold for genes. Metabolites/genes with significant differences in abundance/expression between the HF and LF groups are outside of the threshold line, while those without significant differences are inside the threshold line. Each point represents one metabolite/gene. Blue dots represent DAMs associated with DEGs (up-regulated or down-regulated), while green dots represent DAMs whose associated genes had expression levels that did not differ between the HF and LF groups.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/52cdae395830bc39961012fe.jpg"},{"id":80247795,"identity":"ec3e2dde-4803-44da-a3fc-da534c329818","added_by":"auto","created_at":"2025-04-09 16:16:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":617713,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of DEGs related to flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism and flavonoid DAMs. The pathways indicated by red nodes include sita00941 (flavonoid biosynthesis), sita00940 (phenylpropanoid biosynthesis), and sita00360 (phenylalanine metabolism).\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/52aaa292b73bc1f94ceb0a40.jpg"},{"id":80247780,"identity":"93a1643b-df59-492e-b957-48f6e2bcc00e","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29073,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of flavonoid biosynthesis-related gene expression levels in HF and LF foxtail millet groups. \u003cem\u003eSeita.1G240600\u003c/em\u003e, \u003cem\u003eSeita.6G181000\u003c/em\u003e, and \u003cem\u003eSeita.8G188600\u003c/em\u003e are\u003cem\u003e PAL\u003c/em\u003e, \u003cem\u003ePTAL\u003c/em\u003e, and \u003cem\u003eHCT\u003c/em\u003e genes, respectively. The y-axis presents relative gene expression levels. Each value is the mean of three replicates, and error bars indicate standard deviations. All gene-specific primer sequences are provided in Supplementary Table S4.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/5fdc759748234f3da29ae976.jpg"},{"id":86179738,"identity":"b14d73b2-4395-47e7-b6b0-0828a4fc4b61","added_by":"auto","created_at":"2025-07-07 16:19:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2280395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/a27d721c-9d1c-4456-aeec-7fdb060ed881.pdf"},{"id":80247769,"identity":"ed4fd6c4-240a-41fe-ac2b-9d44fa4f400f","added_by":"auto","created_at":"2025-04-09 16:16:29","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":158199,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1 \u003c/strong\u003eDetails regarding the identified metabolites in the grains of the LF and HF foxtail millet groups; \u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e DAMs between low-flavonoid and high-flavonoid foxtail millet grains; \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e Correlations between DEGs encoding enzymes related to flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism and DAMs in grains; \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e Details regarding qRT-PCR primers; \u003cstrong\u003eSupplementary Fig. S1 \u003c/strong\u003eGO classification of unigenes.\u003c/p\u003e","description":"","filename":"Supplementarydocument.zip","url":"https://assets-eu.researchsquare.com/files/rs-5534545/v1/32222193573bd8201f6c5814.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Targeted metabolomic and transcriptomic analyses provide insights into flavonoid biosynthesis in the grain of foxtail millet","fulltext":[{"header":"Background","content":"\u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e L.), a nutrient-rich cereal domesticated from its wild progenitor (\u003cem\u003eSetaria viridis\u003c/em\u003e) in northern China more than 8,000 years ago, has emerged as a promising functional food crop because of its natural antioxidant properties [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In terms of nutritional components, this drought-tolerant species is similar to major cereals, such as wheat and rice, making it an important source of protein, dietary fiber, and essential minerals [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, foxtail millet accumulates diverse phytochemicals, including flavonoids, phenolic acids, and phytosterols, which collectively contribute to its health-promoting effects [5].\u003c/p\u003e \u003cp\u003eFlavonoids, a major class of plant secondary metabolites, are structurally diverse compounds with varying hydroxylation patterns, oxidation states, and substituent positions, which have been used to divide them into distinct subclasses: anthocyanins, flavanols, flavanones, flavones, flavonols, and isoflavones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These phytochemicals, including flavonoids and phenolic acids, have dual roles as chromatic determinants responsible for grain pigmentation (ranging from white to black) and bioactive agents with antioxidant and anti-inflammatory properties [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Flavonoids exhibit antioxidant, anti-inflammatory, anticancer, cardiovascular protective, and antiviral properties [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and can be used to treat inflammation-related conditions (e.g., arthritis and sore throat). Their anticancer properties reportedly can enhance immunity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To date, 116 flavonoids have been identified in foxtail millet, including catechins, rutin, vitexin, luteolin, kaempferol, and quercetin [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Flavonoid biosynthetic pathways have been thoroughly studied in various plants, including \u003cem\u003eMalus pumila\u003c/em\u003e Mill. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], \u003cem\u003eSalvia miltiorrhiza\u003c/em\u003e [16], \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, and \u003cem\u003eZea mays\u003c/em\u003e [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast to the extensive genomic and metabolomic studies involving major crops (\u003cem\u003eOryza sativa\u003c/em\u003e and \u003cem\u003eZ. mays\u003c/em\u003e) and model organisms (\u003cem\u003eA. thaliana\u003c/em\u003e), there has been limited research on foxtail millet, an increasingly agronomically important drought-tolerant cereal, resulting in fragmented findings and insufficient systemic integration. Previous research on foxtail millet focused on determining nutrient contents, analyzing gene expression via RNA-sequencing (RNA-seq), and cloning and validating functional genes [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Several recent studies combined transcriptomic and metabolomic analyses to explore regulatory mechanisms related to certain functional components. Such studies have been conducted for honeysuckle [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e], cucumber [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e], \u003cem\u003eDendrobium\u003c/em\u003e species [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and peanut [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. He et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported that carotenoid cleavage dioxygenase helps degrade lutein, affecting carotenoid accumulation and color development in foxtail millet grains. In the current study, transcriptomic and metabolomic analyses were conducted to investigate flavonoid biosynthesis in foxtail millet grains. The study findings provide critical genetic resources and molecular markers relevant to breeding nutrient-rich foxtail millet cultivars, thereby enhancing agricultural profitability and socioeconomic sustainability in millet-producing regions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of flavonoid content variations in a recombinant inbred line (RIL) population\u003c/h2\u003e \u003cp\u003eWe determined the total flavonoid content in mature grains obtained from a population comprising 180 foxtail millet RILs. The generated dataset revealed a continuous phenotypic variation in flavonoid accumulation, conforming to a normal distribution (Fig.\u0026nbsp;1A). Accordingly, the foxtail millet grain flavonoid content is a quantitative trait influenced by the environment. We hypothesized that multiple genes co-regulate this trait. Because of the complexity of quantitative traits in cereals and the difficulty in localizing related genes, we constructed an RIL population and then conducted targeted metabolomic and transcriptomic analyses to map foxtail millet flavonoid biosynthesis-related genes. The RILs were ranked on the basis of the grain flavonoid content (from highest to lowest). The 30 lines with the highest flavonoid contents and the 30 lines with the lowest flavonoid contents were selected as a mixed pool. The flavonoid content was approximately 5-fold higher in the high-flavonoid (HF) RIL group than in the low-flavonoid (LF) RIL group (Fig.\u0026nbsp;1B).\u003c/p\u003e \n\u003ch3\u003eIdentification of metabolites\u003c/h3\u003e\n\u003cp\u003eMetabolomic profiles were compared between the HF and LF foxtail millet groups. On the basis of a principal component analysis (PCA), PC1 and PC2 explained 69.30% and 21.50% of the variance in grain metabolomic profiles, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). An orthogonal partial least squares discriminant analysis (OPLS-DA) of the foxtail millet grain metabolite data resulted in R2Y and Q2 values of 0.940 and 0.997, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), which reflected the stability and reliability of the OPLS-DA model. PCA and OPLS-DA score plots revealed the significant separation between the LF and HF groups, indicative of the differences in metabolism between the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification of differentially accumulated flavonoid metabolites\u003c/h3\u003e\n\u003cp\u003eA total of 40 flavonoid metabolites were detected in the LF and HF groups (three biological replicates per line). Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provides detailed information about the detected flavonoid metabolites, including their molecular weight, formula, class, and Kyoto Encyclopedia of Genes and Genomes (KEGG) ID. Flavonoids (32.5%), acids (22.5%), flavanols (12.5%), flavonols (10%), dihydroisoflavones (7.50%), and chalcones (7.50%) were the main flavonoid metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eAfter pre-treatments, such as filtering individual metabolites and simulating missing values in the original data, 36 differentially accumulated metabolites (DAMs) were detected between the HF and LF groups. To functionally characterize flavonoid DAMs and identify their associated metabolic pathways, a KEGG pathway enrichment analysis was conducted, which revealed the flavonoid pathway was significantly enriched among the DAMs in the HF and LF groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These results further confirmed the substantial differences in flavonoid metabolism between the HF and LF foxtail millet groups.\u003c/p\u003e \u003cp\u003eA comparison between the HF and LF groups indicated that among the identified DAMs, 10 were up-regulated and 26 were down-regulated in the HF group. Flavonoid DAMs accounted for most of the up-regulated metabolites in the LF group (Supplementary Table S2). Moreover, flavonoid DAMs, including vanillin, syringaldehyde, (+)-dihydrokaempferol, naringenin chalcone, naringenin, apigenin, protocatechualdehyde, and trans-ferulic acid, were the main up-regulated DAMs (approximately 50%) revealed by the HF \u003cem\u003evs\u003c/em\u003e LF comparison.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTranscriptome analysis\u003c/h3\u003e\n\u003cp\u003eAn RNA-seq analysis was performed to examine the gene expression profiles of HF and LF foxtail millet grains. The raw data were deposited in the National Center for Biotechnology Information (NCBI) database (https://www.ncbi.nlm.nih.gov/sra/PRJNA1183448). The RNA-seq analysis generated 42.81 Gb clean data, with a GC content exceeding 57.3% for each sample. The Phred value at Q30 ranged from 91.58–92.95% (Table 1).\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBasic details regarding RNA-seq data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClean Base(bp)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGC content(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ20(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ30(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.61G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLF-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLF-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.79G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHF-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to a comparison with the LF group (control), 582 genes were differentially expressed in the HF group (146 up-regulated genes and 436 down-regulated genes) (Fig.\u0026nbsp;4). The criteria for identifying differentially expressed genes (DEGs) were as follows: |log\u003csub\u003e2\u003c/sub\u003e(fold-change)| \u0026gt; 1.0 and P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) and KEGG analyses were performed to further clarify DEG functions and their related biological processes. On the basis of a GO analysis, DEGs were annotated with 399 GO terms from the three main categories (molecular function, cellular component, and biological process). The enriched GO terms assigned to DEGs in grains included the following: “movement of cell or subcellular component” (GO:0006928, nine DEGs), “microtubule-based movement” (GO:0007018, nine DEGs), and “chromosome organization” (GO:0051276, eight DEGs) from the biological process category; “nutrient reservoir activity” (GO:0045735, seven DEGs), “protein heterodimerization activity” (GO:0046982, 11 DEGs), “microtubule motor activity” (GO:0003777, nine DEGs), and “microtubule binding” (GO:0008017, nine DEGs) from the molecular function category; and “chromosome” (GO:0005694, three DEGs), “chromosomal part” (GO:0044427, two DEGs), and “extracellular region” (GO:0005576, two DEGs) from the cellular component category (Supplementary Fig. S1). Hence, the DEGs were predominantly associated with subcellular component motility and microtubule dynamics regulation, which is in accordance with the transport mechanisms and antioxidant properties of flavonoid compounds in plants.\u003c/p\u003e\n\u003cp\u003eA KEGG pathway enrichment analysis of the DEGs was conducted [26]; the top 20 enriched KEGG pathways among the DEGs in the HF and LF groups are presented in Fig. 5. Notably, four and two DEGs were involved in phenylpropanoid biosynthesis and phenylalanine metabolism, respectively. Among the DEGs involved in the phenylpropanoid biosynthesis pathway, two \u003cem\u003ePAL\u003c/em\u003e genes (phenylalanine ammonia-lyase) and \u003cem\u003eSPC4\u003c/em\u003e (cationic peroxidase) were up-regulated in the HF group, whereas \u003cem\u003ePOD\u003c/em\u003e (peroxidase), \u003cem\u003eACT\u003c/em\u003e (agmatine coumaroyltransferase), and \u003cem\u003eMet\u003c/em\u003e (methyltransferase) were down-regulated in the HF group (Table 2).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEnrichment of DEGs in the KEGG pathway related to flavonoid biosynthesis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpression patern\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescrition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLog\u003csub\u003e2\u003c/sub\u003e(FoldChange)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUp regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.1G240600\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC101781925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenylalanine ammonia-lyase (\u003cem\u003ePAL\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.96087934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.5G462500\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC111256430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCationic peroxidase SPC4 (CAT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.161535713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.6G181000\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC101766005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenylalanine tyrosine/ammonia-lyase (\u003cem\u003ePTAL\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.461248461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eDown regulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.2G431300\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC111256430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeroxidase 70 (\u003cem\u003ePOD\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.935260709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.8G188600\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC101758480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAgmatine coumaroyltransferase-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.291043079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSeita.8G210100\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLOC101772386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbable inactive methyltransferase Os04g0175900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.516508672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"char\"\u003e\u003cbr\u003e\u003cstrong\u003eVerification of RNA-seq data\u003c/strong\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTo verify the reliability of the RNA-seq data, we selected eight DEGs and determined their transcript levels in the HF and LF groups via a quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The consistency between the DEG expression data generated by qRT-PCR and the RNA-seq data (Fig.\u0026nbsp;6) suggested that the RNA-seq data were reliable and applicable for a molecular analysis of flavonoid biosynthesis and gene mining.\u003c/p\u003e\n\u003ch3\u003eCorrelation analysis of metabolomic and transcriptomic data\u003c/h3\u003e\n\u003cp\u003eCorrelations between DAMs and DEGs were assessed on the basis of a combined analysis of metabolomic and transcriptomic data. We constructed a nine-quadrant diagram to visualize the relationships between DAMs and their associated genes (Spearman\u0026rsquo;s rank correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.8). Most of the DAMs and DEGs in foxtail millet grains were clustered in quadrant 1 (those with positive correlations) and quadrant 7 (those with negative correlations) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further characterize flavonoid biosynthetic pathways in foxtail millet grains, we conducted pathway and regulatory network analyses of DAMs and DEGs related to phenylpropanoid biosynthesis and phenylalanine metabolism (Fig.\u0026nbsp;8). The following three genes, which are the main genes involved in flavonoid biosynthesis, were highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8 or r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.8) with 15 DAMs (Supplementary Table S3): \u003cem\u003eSeita.8G188600\u003c/em\u003e (\u003cem\u003eHCT\u003c/em\u003e), \u003cem\u003eSeita.1G240600\u003c/em\u003e (\u003cem\u003ePAL\u003c/em\u003e), and \u003cem\u003eSeita.6G181000\u003c/em\u003e (\u003cem\u003ePTAL\u003c/em\u003e). Similar correlation analyses of DEGs and DAMs associated with flavonoid biosynthesis and phenylalanine metabolism indicated that \u003cem\u003eSeita.8G188600\u003c/em\u003e (\u003cem\u003eHCT\u003c/em\u003e) was highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8 or r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.8) with 14 flavonoids related to the flavonoid biosynthesis pathway, whereas \u003cem\u003eSeita.1G240600\u003c/em\u003e (\u003cem\u003ePAL\u003c/em\u003e) and \u003cem\u003eSeita.6G181000\u003c/em\u003e (\u003cem\u003ePTAL\u003c/em\u003e) were highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.8 or r\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.8) with six flavonoids related to phenylalanine metabolism (Fig.\u0026nbsp;8 and Supplementary Table S3). PAL activity governs changes in phenylpropanoid metabolism. The resulting cinnamic acid is subsequently hydroxylated to p-coumaric acid, which is then converted to p-coumaroyl-CoA (i.e., essential precursor for flavonoid biosynthesis).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFlavonoid biosynthesis-related gene expression\u003c/h2\u003e \u003cp\u003eA qRT-PCR analysis was completed to verify the relative expression of three genes encoding key enzymes involved in flavonoid biosynthesis in grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The results showed that \u003cem\u003ePAL\u003c/em\u003e (\u003cem\u003eSeita.1G240600\u003c/em\u003e) and \u003cem\u003ePTAL\u003c/em\u003e (\u003cem\u003eSeita.6G181000\u003c/em\u003e) expression levels were significantly up-regulated in the HF group, whereas \u003cem\u003eHCT\u003c/em\u003e (\u003cem\u003eSeita.8G188600\u003c/em\u003e) expression was up-regulated in the LF group. These findings indicate that the expression of flavonoid biosynthesis-related genes, especially \u003cem\u003ePAL\u003c/em\u003e, is induced to varying degrees in foxtail millet grains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious studies on critical metabolic genes in plants involved metabolomic and transcriptomic analyses of different organs [\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31\" citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Flavonoids are major nutrients in crops and regulate several important biological processes. A total of 14 transcription factor-encoding genes and 17 flavonoid biosynthesis-related genes have been identified in the peanut testa [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, very little is known about flavonoid compositions and the molecular mechanisms regulating flavonoid biosynthesis in foxtail millet grains. According to earlier research, the total flavonoid content of red-grain millet is much higher than that of yellow-grain millet [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the current study, the total flavonoid content was 5-fold higher in the HF group than in the LF group (Fig.\u0026nbsp;1B). Candidate genes involved in the accumulation of flavonoids in grains were preliminarily identified following a comparison between the HF and LF groups.\u003c/p\u003e \u003cp\u003eTranscriptome libraries were constructed for HF and LF foxtail millet grains for the subsequent high-throughput sequencing analysis conducted to identify nutrients and their biosynthetic mechanisms. Moreover, flavonoid metabolites in foxtail millet grains were identified using an ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS) system. After sequences were assembled, 582 DEGs in the HF and LF groups were identified. Additionally, 36 flavonoid metabolites in foxtail millet grains were identified using metabolomics techniques. The generated transcriptomic and metabolomic data provide an important basis for future explorations of metabolic pathways in foxtail millet. Flavonoids are one of the major nutritional components of foxtail millet grains. According to a KEGG analysis, flavonoid metabolites that accumulate differentially in foxtail millet grains include flavonoids, phenylpropanoids, phenylalanine, dihydroflavone, isoflavones, and flavonoid carbonoside anthocyanins; the associated biosynthetic pathways were among the significantly enriched KEGG pathways. This is crucial information for elucidating the molecular mechanisms regulating flavonoid accumulation in foxtail millet grains.\u003c/p\u003e \u003cp\u003eA metabolomic analysis of foxtail millet grains detected 10 flavonoid metabolites that were more abundant in the HF group than in the LF group, including naringenin, naringenin chalcone, apigenin, vanillin, syringaldehyde, trans-ferulic acid, and phthalic acid. Naringenin was recently revealed to have numerous biological effects (e.g., antioxidant, anti-inflammatory, and metabolic regulatory effects). Several flavonoids isolated from the peanut testa, such as cyanidin, peonidin, and pelargonidin, reportedly have significant anti-inflammatory, immune-enhancing, and anticancer properties [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Apigenin has some medicinal properties. More specifically, it can decrease the risk of cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e36\u003c/span\u003e], has neuroprotective properties [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and lowers blood glucose and lipid levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The consumption of foxtail millet grains has beneficial effects on health. However, our analysis of the metabolites in foxtail millet grains demonstrated that metabolites related to flavonoid biosynthesis accumulate differentially in the HF and LF groups, suggesting that appropriately modifying different grain groups may lead to improved flavonoid use and efficacy. These findings are relevant to future evaluations of crop plants, with potential implications for the applicability of different foxtail millet groups.\u003c/p\u003e \u003cp\u003eA lack of genetic data has delayed research on metabolic pathways in foxtail millet. To further elucidate the regulation of flavonoid accumulation in different foxtail millet groups, DEGs in HF and LF grains identified following an RNA-seq analysis were subjected to a KEGG pathway enrichment analysis. The results revealed the enrichment of flavonoid biosynthesis, flavone and flavonol biosynthesis, isoflavonoid biosynthesis, and upstream phenylpropanoid biosynthesis pathways in all samples. The basic flavonoid metabolic pathways in plants have been characterized, with various genes (e.g., \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e, \u003cem\u003eCYP75A\u003c/em\u003e, \u003cem\u003eANR\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, and \u003cem\u003eDFR\u003c/em\u003e) encoding proteins with key roles in the synthesis of bioactive components in plants [\u003cspan additionalcitationids=\"CR40 CR41 CR42 CR43 CR44 CR45 CR46\" citationid=\"CR38\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the present study, \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003ePTAL\u003c/em\u003e expression levels were higher in HF grains than in LF grains (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which led to the increased accumulation of phenylalanine ammonia-lyase. The results of the UPLC-ESI-MS/MS-based analysis were also confirmed (Supplementary Table\u0026nbsp;3). Phenylalanine ammonia-lyase is a key enzyme in the flavonoid biosynthesis pathway, but the enzymes encoded by different \u003cem\u003ePAL\u003c/em\u003e genes may have the same or different functions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In the current study, \u003cem\u003ePAL\u003c/em\u003e transcript levels were positively correlated with apigenin, vanillin, and naringenin contents, but \u003cem\u003ePTAL\u003c/em\u003e transcript levels were negatively correlated with salicylic acid and catechin contents (Supplementary Table S3). In \u003cem\u003eA. thaliana\u003c/em\u003e, \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003ePTAL\u003c/em\u003e genes encode proteins with important functions related to flavonoid biosynthesis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The main flavonoids that accumulated more in the HF group than in the LF group were naringenin and aldehyde derivatives, such as naringenin chalcone, vanillin, and syringaldehyde, suggesting that \u003cem\u003ePAL\u003c/em\u003e catalyzes the conversion of chalcone to naringenin. Other substrates that are specifically bound by \u003cem\u003ePAL\u003c/em\u003e are ferulic acid, vanillic acid, and syringic acid [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eHCT\u003c/em\u003e, which belongs to the plant acyltransferase family, catalyzes reactions involving various substrates, such as quercetin, kaempferol, quinic acid, and gentianic acid, leading to the formation of ester or amide compounds. It has a central role in the biosynthesis of chlorogenic acid and lignin [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The roles of \u003cem\u003eHCT\u003c/em\u003e in lignin biosynthesis were recently validated, while its involvement in the synthesis of chlorogenic acid and flavonoids has also been determined. In \u003cem\u003eA. thaliana\u003c/em\u003e, inhibited \u003cem\u003eAtHCT\u003c/em\u003e expression is accompanied by a decrease in lignin accumulation, but an increase in flavonoid levels [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In foxtail millet, \u003cem\u003eHCT\u003c/em\u003e catalyzes the conversion of p-coumaroyl-CoA to caffeoyl-CoA, which is subsequently converted to lignin precursors by a transferase. In the present study, \u003cem\u003eHCT\u003c/em\u003e expression was significantly down-regulated in the HF group, thereby disrupting the synthesis and accumulation of lignin precursors. This, in turn, promoted the synthesis and accumulation of flavonoids via an alternative metabolomic pathway, with naringenin chalcone production being significantly up-regulated.\u003c/p\u003e \u003cp\u003eThe results of the transcriptomic and targeted metabolomic analyses performed in this study provide new information about flavonoid biosynthesis in cereals. However, additional research is required to comprehensively clarify the specific mechanisms regulating flavonoid compositions and contents in foxtail millet. In general, flavonoid synthesis in foxtail millet involves dynamic interactions among multiple regulatory networks. Ultimately, flavonoid compositions and contents in foxtail millet grains depend on the coordinated effects of the overall regulatory network.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe total flavonoid content in foxtail millet grains was significantly higher in the HF group than in the LF group. Comparisons of the transcriptomic and targeted metabolomic profiles of the HF and LF foxtail millet groups revealed the factors influencing flavonoid accumulation and their related candidate genes. Notably, \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003ePTAL\u003c/em\u003e, and \u003cem\u003eHCT\u003c/em\u003e were identified as genes encoding key regulators of flavonoid biosynthesis in foxtail millet. Both \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003ePTAL\u003c/em\u003e positively regulate flavonoid accumulation, while \u003cem\u003eHCT\u003c/em\u003e negatively regulates flavonoid synthesis and accumulation. These study results provide new insights relevant to breeding and selecting foxtail millet lines with improved nutrient profiles.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and sample collection\u003c/h2\u003e \u003cp\u003eThe RIL population included in this study was derived from a cross between the HF variety \u0026lsquo;Jinmiaohongjiugu\u0026rsquo; (maternal parent) and the LF variety \u0026lsquo;Yugu28\u0026rsquo; (paternal parent). The RIL population consisted of 325 lines, with 180 lines selected for the screening of HF and LF lines based on the grain flavonoid content at maturity. The 30 lines with the highest grain flavonoid contents were included in the HF group, whereas the 30 lines with the lowest grain flavonoid contents were included in the LF group. Three biological replicates of mature grain samples were collected and immediately frozen in liquid nitrogen before being stored at \u0026minus;\u0026thinsp;80\u0026deg;C until the subsequent transcriptomic and metabolomic analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of grain flavonoid contents\u003c/h2\u003e \u003cp\u003eGrain flavonoid contents were determined for the 180 selected RILs using a plant flavonoid content determination kit (Beijing Solarbio Science \u0026amp; Technology Co., Ltd., Beijing, China). Millet grain samples were dried and pulverized and then passed through a 50-mesh sieve. Approximately 0.1 g powdered sample was added to 1 mL extraction solution, after which the mixture was sonicated (300 W for 30 min at 60\u0026deg;C). The mixture was centrifuged (12,000 rpm for 10 min at 25\u0026deg;C) and the supernatant was retained. Test reagents were mixed thoroughly and then incubated in a water bath (37\u0026deg;C) for 45 min. The mixture was centrifuged (1,000 rpm for 10 min at room temperature) and the supernatant was retained. The absorbance of the supernatant was measured at 470 nm, after which a rutin standard curve was used to determine the flavonoid content. This analysis was completed three times per sample. The rutin standard curve was prepared by diluting a rutin standard solution (10 mg/mL) to 1.5, 1.25, 0.625, 0.3125, 0.15625, 0.078, 0.039, and 0.02 mg/mL using a standard diluent. SPSS software was used to analyze the grain flavonoid contents of 180 RILs, whereas Excel software was used to select 30 lines with low grain flavonoid contents and 30 lines with high grain flavonoid contents to construct a mixed pool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTargeted metabolomic profiling and analysis\u003c/h2\u003e \u003cp\u003eSample preparation and extraction: Frozen plant samples (100 mg each) were sonicated for 30 min using 500 \u0026micro;L 80% (v/v) methanol containing 0.2% (w/v) vitamin C. The mixture was centrifuged (12,000 rpm for 10 min) and the supernatant was retained. These steps were repeated twice and the resulting supernatants were combined.\u003c/p\u003e \u003cp\u003eUPLC conditions: Sample extracts were analyzed using a UPLC-Orbitrap-MS system (UPLC, Vanquish; MS, QE; Thermo Fisher Scientific, Waltham, MA, USA). Analytical conditions were as follows: UPLC: column, Waters HSS T3 (50 \u0026times; 2.1 mm, 1.8 \u0026micro;m); column temperature, 40\u0026deg;C; flow rate, 0.3 mL/min; injection volume, 2 \u0026micro;L; solvent system, water (0.1% v/v acetic acid):acetonitrile (0.1% v/v acetic acid); gradient program, 90:10 (v/v) at 0 min, 90:10 (v/v) at 2.0 min, 40:60 (v/v) at 6.0 min, 40:60 (v/v) at 8.0 min, 90:10 (v/v) at 8.1 min, and 90:10 (v/v) at 12.0 min [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLC-MS/MS analysis: HRMS data were recorded using a Q Exactive hybrid Q-Orbitrap mass spectrometer equipped with a heated ESI source (Thermo Fisher Scientific) according to fullms-ms2 acquisition methods. ESI source parameters were as follows: sheath gas, 40 arb; auxiliary gas, 10 arb; spray voltage, \u0026minus;\u0026thinsp;2,800 V; temperature, 350\u0026deg;C; and ion transfer tube temperature, 320\u0026deg;C. The single ion monitor mode (negative ion mode) was applied [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePublic metabolite databases (e.g., MassBank, KNAPSAcK, HMDB, and METLIN) and the MVDB v2.0 database from Smart Biotechnology Co., Ltd. (Tianjin, China) were used for the qualitative analysis of primary and secondary mass spectrometry data. Accordingly, structural characteristics of the metabolites were determined. For the quantitative analysis of metabolites, MRM was used and PCA and OPLS-DA were performed to identify DAMs. We divided foxtail millet lines into LF and HF groups for a comparative analysis. The following thresholds were used to identify significant DAMs: VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and |log\u003csub\u003e2\u003c/sub\u003e(HF group/LF group)| \u0026ge; 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and transcriptomic analysis\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated and purified from foxtail millet grains using a Complex Plant RNA Rapid Extraction Kit (Beijing Labhelper Biotechnology Co., Ltd., Beijing, China). Samples were treated with an RNase-free DNase I digestion kit (Aidlab, Beijing, China) to remove genomic DNA contaminants. RNA degradation was assessed using 1% agarose gels, while RNA concentrations were determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA).\u003c/p\u003e \u003cp\u003eAfter verifying the quality of the constructed libraries using a 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA), they were sequenced using an Illumina HiSeq\u0026trade; 2000 system (Illumina, San Diego, CA, USA). Raw data were filtered by removing reads with adapter sequences and low-quality reads using fastp software [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The remaining clean reads were assembled to obtain unigenes, which were functionally annotated on the basis of NCBI non-redundant protein sequences (Nr; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fp.ncbi.nlm.nih.gov/blast/db/FASTA/\u003c/span\u003e\u003cspan address=\"https://fp.ncbi.nlm.nih.gov/blast/db/FASTA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as well as sequences in the KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Clusters of Orthologous Groups of proteins (COG; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/COG/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/COG/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneontology.org\u003c/span\u003e\u003cspan address=\"https://www.geneontology.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Swiss-Prot (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/uniprot/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/uniprot/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and translation of EMBL (TrEMBL) databases. Similarly, we conducted a comparative analysis of the HF and LF groups. DEGs were screened using the following criteria: |log\u003csub\u003e2\u003c/sub\u003e(fold-change)| \u0026gt; 1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. DEG biological functions and associated metabolic pathways were determined using GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://geneontology.org/\u003c/span\u003e\u003cspan address=\"http://geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated analysis of metabolomic and transcriptomic data\u003c/h2\u003e \u003cp\u003eTranscriptomic and metabolomic data for the grains in the HF and LF foxtail millet groups were analyzed. DEGs related to flavonoid biosynthesis and flavonoid DAMs in each comparison group (three replicates each) were subjected to a correlation analysis. Spearman\u0026rsquo;s correlation coefficients were used to determine differences between flavonoid biosynthesis-related genes and transcription factor genes (correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.90 and P\u0026thinsp;\u0026le;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR analysis\u003c/h2\u003e \u003cp\u003eOn the basis of metabolomic, transcriptomic, and combined analyses, eight candidate DEGs identified by the RNA-seq analysis were selected for a qRT-PCR analysis. RNA was reverse transcribed to cDNA using a PrimeScript\u0026trade; RT reagent kit (Tolo Biotech, Shanghai, China). For the qRT-PCR analysis, 2\u0026times; Q3 SYBR qPCR Master Mix (Tolo Biotech) and a CFX96 Real-Time PCR Detection System (Bio-Rad, USA) were used. Gene-specific qRT-PCR primers (Supplementary Table S4) were designed using Beacon Designer 8.0. The qRT-PCR conditions were as follows: pre-denaturation at 95\u0026deg;C for 30 s; 40 cycles of 95\u0026deg;C for 30 s, 59\u0026deg;C for 30 s, and 72\u0026deg;C for 30 s. Relative gene expression levels were calculated according to the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method, with three biological replicates per gene [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTranscriptomic and metabolomic analyses were completed using three biological replicates. Statistical analyses were performed using SPSS 22.0 software (IBM, Chicago, IL, USA). An independent samples \u003cem\u003et\u003c/em\u003e-test was used for the statistical comparison of HF and LF groups.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAdditional files\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u0026nbsp;\u003c/strong\u003eDetails regarding the identified metabolites in the grains of the LF and HF foxtail millet groups; \u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e DAMs between low-flavonoid and high-flavonoid foxtail millet grains; \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e Correlations between DEGs encoding enzymes related to flavonoid biosynthesis, phenylpropanoid biosynthesis, and phenylalanine metabolism and DAMs in grains; \u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e Details regarding qRT-PCR primers; \u003cstrong\u003eSupplementary Fig. S1\u0026nbsp;\u003c/strong\u003eGO classification of unigenes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.Q. and Z.Y.\u0026nbsp;are co-first authors with equal contributions, and conceived and designed the research. S.F., C.Z., Y.J.\u0026nbsp;acquired the data. N.Q., Z.Y., H.Z. and S.D.\u0026nbsp;analyzed the data. N.Q., Z.Y., and L.J.\u0026nbsp;drafted and revised the manuscript. L.\u0026nbsp;J., C.W.\u0026nbsp;and X.W.\u0026nbsp;extracted and analyzed samples. All authors agreed to publish the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Scientific Research Plan Joint Fund of Henan Province (232301420105), China Agricultural Research System of the Ministry of Finance and Ministry of Agriculture and Rural Affairs (CARS-06), the Key Research and Development Projects of Henan Province (231111110300, 241111112100), the Funding of Joint Research on Agriculture Varieties of Henan Province (2022010401), and Innovation team of Henan Academy of Agricultural Sciences (2024TD39).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article has been deposited in the National Center for Biotechnology Information DataBase (NCBIdb, https://www.ncbi.nlm.nih.gov/sra/PRJNA1183448) under the accession number PRJNA1183448.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Xianmin Diao from China Academy of Agriculture Sciences for improving the article\u0026rsquo;s data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Cereal Crop Research Institute, Henan Academy of Agricultural Sciences, Postgraduate T\u0026amp;R Base of Zhengzhou University, Zhengzhou 450002, China. \u003csup\u003e2\u003c/sup\u003e School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China. \u003csup\u003e3\u003c/sup\u003e Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China. \u003csup\u003e4\u003c/sup\u003e Nanyang Academy of Sciences, Nanyang 473000, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWei W, Li S, Wang, Y, et al. 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Methods,\u003cem\u003e \u003c/em\u003e2001, 25: 402\u0026ndash;408.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Foxtail millet, Targeted metabolomics, Transcriptomics, Flavonoid biosynthesis","lastPublishedDoi":"10.21203/rs.3.rs-5534545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5534545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFoxtail millet (\u003cem\u003eSetaria italica\u003c/em\u003e L.), a traditional Chinese crop, is valued for its considerable abundance of compounds with health benefits (e.g., flavonoids). Despite the nutritional significance of flavonoids, their biosynthetic pathways in foxtail millet remain poorly characterized. In this study, we integrated targeted metabolomic and transcriptomic analyses to systematically elucidate the flavonoid biosynthesis pathway and identify genes encoding key regulators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eQuantitative profiling of a foxtail millet recombinant inbred line (RIL) population revealed differences in the grain flavonoid content, with flavonoid levels 5-fold higher in the high-flavonoid (HF) group than in the low-flavonoid (LF) group. Targeted metabolomic and transcriptomic analyses revealed key regulatory networks controlling flavonoid biosynthesis in foxtail millet. A comparative transcriptomic analysis detected significant differences in the expression profiles of flavonoid biosynthesis-related genes between the HF and LF groups. According to a targeted metabolomic analysis, the concentrations of 10 distinct flavonoids were significantly higher in the HF group than in the LF group. Integrated analyses indicated that genes encoding shikimate O-hydroxycinnamoyl transferase (\u003cem\u003eHCT\u003c/em\u003e), phenylalanine ammonia-lyase (\u003cem\u003ePAL\u003c/em\u003e), and phenylalanine/tyrosine ammonia-lyase (\u003cem\u003ePTAL\u003c/em\u003e) are crucial for the observed differences in the flavonoid contents of the HF and LF groups.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings provide insights into the genetic regulation of flavonoid metabolism in foxtail millet. Furthermore, this study identified candidate genes that may be useful molecular targets for breeding foxtail millet varieties with optimal nutritional quality.\u003c/p\u003e","manuscriptTitle":"Targeted metabolomic and transcriptomic analyses provide insights into flavonoid biosynthesis in the grain of foxtail millet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-09 16:16:24","doi":"10.21203/rs.3.rs-5534545/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-28T06:45:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T07:54:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T08:32:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182937086945442272891074572981752589403","date":"2025-04-07T01:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189705147345546128818743949834390204850","date":"2025-04-06T05:54:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-05T17:02:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-05T12:44:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-04-02T14:17:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a68aba1f-a663-40c9-a2d1-ac9d0064bc76","owner":[],"postedDate":"April 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:10:44+00:00","versionOfRecord":{"articleIdentity":"rs-5534545","link":"https://doi.org/10.1186/s12864-025-11780-x","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-07-01 15:57:47","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-09 16:16:24","video":"","vorDoi":"10.1186/s12864-025-11780-x","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11780-x","workflowStages":[]},"version":"v1","identity":"rs-5534545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5534545","identity":"rs-5534545","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}