Integrative Analysis of Flavonoid Pathways in Rice: Enhancing Heat Tolerance

Integrative Analysis of Flavonoid Pathways in Rice: Enhancing Heat Tolerance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative Analysis of Flavonoid Pathways in Rice: Enhancing Heat Tolerance Xiaojie Wu, Lingfang Yang, Jinbo Han, Hanqing Liu, Gaokun Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5406993/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Sep, 2025 Read the published version in BMC Genomics → Version 1 posted 4 You are reading this latest preprint version Abstract Background Plants tend to produce special metabolites to resist biotic or abiotic invasions, in which flavonoid-mediated defense responses play an important role. Result In our previous work, the rel1 -D mutant was obtained by T-DNA insertion. Nearly all ZH11 died after 42 ℃ treatment, while nearly half of the mutants survived. By transcriptomic and metabolomic analysis of leaves, 1184 differentially expressed genes (DEGs) and 126 differentially accumulated metabolites (DAMs) were identified, most of these DEGs and DAMs were enriched in biosynthesis-related pathways such as the L-Phenylalanine pathway, flavonoid biosynthesis pathway and phenol pathway. Furthermore, a correlation network involved phenotypic traits was constructed based on the genes and metabolites. Conclusion Potential genes regulated by REL1 and flavonoid metabolites were identified. REL1 may affect the accumulation of flavonoid metabolites by regulating the expression of key genes in flavonoid biosynthesis pathway to influence the heat tolerance of rice. transcriptome metabolome REL1 flavonoids heat-tolerence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Temperature plays an important role in the development, yield and quality of rice. When the environmental temperature exceeds the critical value, the growth and development of rice are influenced. Statistical data shows that when the maximum daily temperature exceeds 38 ℃, it is known as high temperature, which is a threat to the development of rice [ 1 ]. Flavones are important secondary metabolites of phenols. Rice (Oryza sativa) is one of the most important crops in the world [ 2 ]. A comprehensive understanding of biological processes such as plant development and stress resistance is critical for achieving high yield and quality in rice. Rice is known to contains a variety of flavonoids compounds, which are secondary metabolites, including chalcones, flavanoids, flavones, and anthocyanins [ 3 ]. Recently, the stress resistance of flavonoid plants has been done of many researches, with an emphasis on their ability to resist high temperatures, drought, cold, and diseases being highlighted [ 4 , 5 ]. The production of flavonoids involves a multifaceted process known as the phenylpropionic acid pathway. It belongs to one of the largest families of secondary metabolites of polyphenols worldwide and is widely distributed in various plant organs and tissues [ 6 ]. The six primary classes of flavonoids are as follows: isoflavones, flavonoids, flavanones, flavonols, and anthocyanins [ 7 , 8 ]. Flavonoid, the most biologically active secondary metabolite of plant, has been extensively studied in recent years. It has been shown that these compounds play important roles in plant growth, development, reproduction, and stress tolerance [ 9 , 10 ]. Flavonoids are antioxidants [ 11 – 13 ], scavenging reactive oxygen (Ros) [ 14 ], and they seem to be involved in protecting plants from infection, animal feeding, and stresses from both biotic and abiotic factors such as drought and thermal stress [ 15 ]. Moreover, flavonoids are known to possess a multitude of biological activities, including being antiviral, anti-cancer, anti-allergic, anti-inflammatory, and in the prevention of coronary heart disease, and they are endowed with significant nutritional and physiological benefits [ 17 ]. Thus, increasing the amounts of flavonoids is arguably more important for crop safety and human nutrition. To date, more than 9000 different flavonoid molecules have been discovered in many plant species [ 18 ]. Rice is the most economically important staple crop for human nutrition and serves as an experimental system for monocots [ 19 ]. In contrast to dicotyledones such as Arabidopsis and Tomato [ 20 ], the biosynthetic pathways of flavonoids in rice are relatively unknown. According to several pioneering studies [ 21 – 23 ], flavonoids are produced from L-Phenylalanine via the phenylpropionic acid pathway, while L-Phenylalanine is synthesized through the shikimic acid pathway [ 12 ]. The first three steps in the phenylpropanoid pathway are referred the general phenylpropanoid pathway [ 24 ]. In addition, PAL plays a key role in mediating carbon flux from primary to secondary metabolism in plants [ 25 ]. The first two processes in the flavonoid biosynthetic pathway are facilitated by chalcone synthase ( CHS ) and Chalcone isomerase ( CHI ) to form Chalcone [ 26 ] and the sequential production of flavanones (including Naringin) [ 27 ]. In rice, flavanones are converted to the corresponding flavones by OsFNS I-1 or OsFNS II in vitro or in vivo [ 26 , 28 , 29 ]. Dihydrokaempferol is synthesized by flavanone 3-hydroxylase ( F3H ) [ 30 ]. The Flavonol synthase ( FLS ) then converts the dihydroflavonols into flavonols by desaturation of the dihydroflavonols [ 31 ]. In Rice, OsFLS is a bifunctional dioxygenase that exhibits FLS and F3H activity to exercise its function in flavonol production [ 32 ]. In typical cells, the flavonoid pathway can account for approximately 20% of the total carbon flux in plants [ 20 ]. The process of flavonoid metabolic diversity in plants plays an important role in plant adaptation to environmental diversity. The structural variety of flavonoids has a major role in determining their biological activity. The capacity to gather stocks of metabolites unique to a species has been rapidly enhanced with the advent of metabolomics [ 33 ]. Since the full genome sequencing of rice was completed in 2005, it has been established as a favored model organism in the field of functional genomics, attributed to its relatively compact genome size (~ 389 Mb) [ 34 ]. However, there has not been a complete and systematic identification and investigation of the key structural genes involved in the biosynthesis of flavonoid scaffolds, except for those related to CHS gene families [ 35 , 36 ]. Therefore, the study will focus on rice, with the relative abundance of each flavonoid and the changes in the expression of key regulatory genes being systematically analyzed using transcriptomics and metabonomics. This study provides a theoretical basis for further studies on the mechanism of rice functional components and the improvement of rice quality. Results Significant morphological differences were observed between these two varieties. Compared with the wild type ZH11 (WT), rel1 -D exhibited rolled leaves, a reduction in plant height, shorter ear length, fewer tillers and narrower leaf width. Additionally, the leaf roll index and leaf bending were significantly higher in rel1 -D, while the maximal leaf width, 10-grain width, 10-grain length and 100-grain weight did not change significantly [ 37 ]. Phenotype analysis of heat stress in rice seedlings The regreening phenotype of ZH11 and rel1 -D rice seedlings under high temperature (42 ℃) was shown in Fig. 1A. No significant difference in appearance was observed between ZH11 and rel1 -D after high temperature treatment for 48 h. The survival of the rel1 -D mutant leaf was observed to increase with the extention of recovery time, while ZH11 was dead on the 6th day (Fig. S1 ), which indicated that rel1 -D could tolerate high temperature and recover from heat damage. The content of superoxide anion in cells was determined by nitrotetrazolium blue chloride (NBT), and cell death was evaluated by using trypan blue staining (TAB). It was shown by NBT staining that superoxide anions had beed secreted in both ZH11 and rel1 -D after 0 days of high temperature treatment (Fig. 1B), indicating that both of them were under stress. Almost all the cells in ZH11 were shown by TAB staining to died at 0 day after hyperthermia, but fewer in rel1 -D (Fig. 1B). Figure 1 Phenotypes of ZH11 and rel1 -D mutants under high temperature treatment. (A), 30℃ is thenormal growth temperature, 42℃ represents high temperature treatment, and 0,2,4,6 days represent the time of returning to normal temperature after heat stress. (B), NBT and TAB staining were performed on the materials recovered on the 0 day. Metabolom analysis A total of 793 metabolites were identified across two groups using a targeted metabolomics approach. Excellent reproducibility was demonstrated by the analysis of three bioreplicates per sample (Fig. 2 A). In the main component analysis, the metabolites of 528 hybrid combinations were classified into PCA1 (41.7%) and PCA2 (20.27%), which were associated with a distinct spectrum (Fig. 2 B). Among them, 82 of them, 116, 46, 149, 30, 7, 66, and 73 (Fig. 2 C, Table S1 ). It was found that flavonoids, lipids and phenols were the most abundant. The flavonoid category includes chalcones, dihydroflavone, dihydroflavonol, flavanols, flavonoids, flavonoid carbonoside, and flavonols, with flavonoids making up 47.65% of the total (Fig. 2 D). The metabolic changes of mutant rel1 -D and ZH11(WT) were studied by metabonomics. In this paper, the differential accumulation of DAMs (DAMs) was investigated in the tillering period (Fig. 3A). A total of 126 differential metabolites were identified, of which 75 were upregulated and 51 were downregulated (Fig. 3B and Table S2). Co-association KEGG enrichment analysis revealed that rel1 -D co-maps with ZH11(WT) in six pathways (Table S3), and interestingly, among these synthetic pathways, the flavonoid pathway and phenypropanoid biosynthesis were highly enriched (Fig. 3C, D). The results showed that threr was a positive correlation between these genes and the regulation of metabolites, suggesting that these genes might positively influence gene regulation (Fig. 3E). The highly correlated DAMs and DEGs were shown in the heat map. Figure 3. Integration analysis of differential metabolites and differential genes. (A), volcano plot of DAMs. (B), Relative abundance of DAMs. (C), Differential metabolite classification plot. (D) Pathway enrichment analysis of DAMs and DEGs. (E) Correlation analysis of DAMs and DEGs. The difference in gene expression of the two materials Transcriptome sequencing was conducted on six samples, resulting in 39.04 GB of clean data, with each sample yielding over 6.14 GB. The Q30 rate was higher than 94.49% (Table S4). Each sample's clean reading was aligned with the specified reference genome, achieving an alignment ratio of 87.72–96.27% (Table S5). The Cluster Analysis Heatmap showed that there was a strong bioreproducibility in all three varieties. In this study, 1184 DEGs were identified, of which 396 were up-regulated and 395 were down-regulated (Fig. 4 A, B). The GO annotation analysis showed 16 DEGs were classified into biological processes and molecular functions (Fig. 4 C), with the majority of them being related to molecular and metabolic processes. Among the molecular processes, hydrolases were the most prevalent. The KEGG enrichment analysis showed that DEGs were significantly present in the pathways associated with the biosynthesis of flavonoids and the biosynthesis of secondary metabolites (Fig. 4 D, Table S6). These results suggest that the rel1 -D mutant influences the flavonoid pathway. qRT-PCR was used to validate the transcriptome data. In order to validate the results of the RNA-Seq study, qRT-PCR was performed to confirm the results of the transcriptome. A selection of random genes, including OsREL1 , OsIRL , OsNAC4, OsROD1, OsCHS1, OsWAK14, OsWRKY67, OsPAL, OsCHIL2, Os4CL2 , and OsF3H2 , were analyzed in the tillering leaves of ZH11 and rel1 -D (Fig. 5 ). The results showed that the RNA-Seq and qRT-PCR results did not differ significantly, which showed a similar pattern. As a result, the reliability of the RNA-Seq data used in this study was verified. Coexpression networks reveal differential regulation of key genes, metabolites and physiological traits A co-expression network was constructed to explore the potential coexpression network of flavonoids, which included 581 genes and 126 differential transcription factors (PCCs). After the optimum parameter (β = 5) was determined, the WGCNA algorithm (Fig. S2 A, B) was used to transform the correlation coefficients of each gene pair into neighboring coefficients. Subsequently, WGCNA was carried out to further correlate gene expression patterns with flavonoid biosynthesis and accumulation. The genes were separated into 11 different modules, and the Pearson correlation coefficient was used to determine the correlation between the genes of different modules. The darker the colour, the higher the correlation of the module is (Figure S2 C, Figure S3). Therefore, the dark module could be used to predict the coexpression network of flavonoids in rice. In order to know the regulatory network of the critical genes, metabolites and physiological forms of the mutants, 36 genes, 82 metabolites and 10 physiological characteristics were analyzed by Pearson (Table S7). Regulatory network pairs were visualized in cytoscape based on pearson correlation > 0.8 as a threshold (Fig. 6 ). The results indicated that there were 128 nodes connected to 3884 edges. 1,840 genes were found to show a negative correlation, and 2,44 were found to show a positive correlation. All in all, there was a strong positive correlation between the metabolism of flavonoids and phenols and the genes of the leaves. DEGs and DAMs in the biosynthesis of flavonoids Through enrichment of the KEGG pathway and gene ontology functional analysis, we identified 21 DEGs encoding flavonoid and phenolic acid biosynthetic enzymes, of which 1 was OsPAL DEG, 1 was OsC4H DEG, 2 were Os4CL DEGs, 2 were OsCHS DEGs, and 4 were OsCHI DEGs, there were 3 OsF3H DEGs, 1 OsFLS DEG, 2 OSUGT DEG, 1 OsDFR DEG, 1 OsANS DEG, 1 OsLAR DEG, 2 OSGT1 DEGs and 1 OsHIDH DEG. In order to investigate the mechanism of flavonoid accumulation in rice, we studied the gene expression patterns of both phenylpropane and flavonoids. The phenylpropanoid pathway provides precursors for flavonoid biosynthesis, including phenylalanine. The first two reactions of the flavonoid pathway were catalyzed by PAL and CHS , followed by diverse downstream reactions catalyzed by a series of enzymes, resulting in the aglycones biosynthesis (Fig. 7 A). The high expression of OsPAL , OsC4H , and Os4CL produced enough precursors to synthesize flavonoids by utilizing Os4CL to produce p-coumaroyl-CoA. This might be one of the reasons for higher proportions of flavanones, flavones, flavonols, isoflavones, and flavanols in rel1 -D. Flavonoids were synthesized from naringenin, a substance catalyzed by CHI . In this study, the increased expression of OsCHI in rel1 -D led to the highest accumulation of naringenin, with the downstream flavones, flavonols, isoflavones, and flavanols also exhibiting the highest accumulation in rel1 -D, which could be attributed to the high expression of the OsF3H genes in rel1 -D. The transcription factors OsMYB1, OsMYB2, OsMYB4, OsMYB38, OsMYB102, OsHLH, OsMPS, OsDCD 20.2 were highly expressed in rel1 -D. Predictions from the AlphaFold model ( https://golgi.sandbox.google.com/ ) showed that the protein structures of transcription factors OsHLH, OsMPS and OsCDC 20.2 interacted (Fig. 7 B). Among the promoters of OsPAL, OsC4H, Os4CL, OsCHI, OsF3H, OsFLS, OsLAR, OsUGT, OsGT14, OsANS, OsDFR , etc., several binding original promoters of MYB, bHLH, WD40 (Fig. 7 C) were found, which showed that these genes and transcription factors regulated the synthesis of flavonoids in plants. The transcription level of OsFLS was higher in rel1 -D, and the contents of myricetin, quercetin, kaempferol, astragalin, quercitrin, rutin, iso­rhamnetin, quercetin 3-glucoside, and kaempferol 3-O-rutinoside in rel1 -D were significantly higher than that in other stages. This result implied that the high expression of OsPAL , OsC4H , Os4CL , OsCHS , OsCHI , OsF3H , and OsFLS were essential for the accumulation of effective components of flavonoids (flavanones, flavones, and flavonols) in rel1 -D, which suggests that these genes are required for the biosynthesis of these compounds. A Pearson’s correlation analysis was executed to examine the correlation between the expression levels of OsPAL , OsC4H , Os4CL , OsCHS , OsCHI , OsF3H , and OsFLS of flavonoid biosynthesis pathway and relative contents of the 70 differentially expressed flavanones, flavones, and flavonols shown in Table S7 (Pearson’s correlation coefficient ≥ 0.8 and P < 0.05). The results showed that 4 DEGs for OsCHI (Os03g0132900, Os10g0416500, Os12g0115700, Os11g0116300), 3 DEGs for OsF3H (Os04g0376500, Os04g0667200, Os04g0662600), 1 DEG for OsFLS (Os02g0767300), 1 DEG for OsCHS (Os11g0530600), 1 DEG for OsPAL (Os02g0626100), 2 DEGs for Os4CL (Os02g0697400, Os02g0177600) were significantly correlated with dihydroquercetin, hyperoside, kaempferol, kaempferol-3-O-rutinoside, luteolin, luteoloside, myricetin, naringenin, nobiletin, quercetin, quercetin 3-glucoside, rutin, and tiliroside (Fig. 7 D). This result suggested that these 9 DEGs were the key contributors to the accumulation of effective components in rel1 -D. Discussion It was found that the rel1 -D mutant had a higher ratio of green and survival than ZH11(Fig. 1, Fig. S1 ), RNA-seq and metabolome were used to analyze two rice varieties at tillering stage. To study the molecular mechanism of rice resistance to high temperature can improve the understanding of rice regulation mechanism under abiotic stress, and provide theoretical basis for the subsequent breeding and screening of high temperature tolerant varieties. Flavonoids are one of the most important groups of secondary metabolites, which play a key role in plant biology, including resistance to pathogenic agents, growth regulation, and root development [ 38 – 42 ]. Several enzymes involved in flavonoid biosynthesis have been identified, such as OsPAL DEG, OsC4H DEG, Os4CL DEG, OsCHS DEG, OsCHI DEG, OsF3H DEG, OsFLS DEG, OsUGT DEG, OsDFR DEG, OsANS DEG, OsLAR DEG, OsGT1 DEG, and OsHIDH DEG. Within the flavonoid biosynthesis pathway, CHS facilitates the isomerization of important intermediates into flavones and flavonols [ 43 ]. The elevated expression of OsPAL , OsC4H , and Os4CL in rel1 -D leads to the production of p-coumaryl-CoA, which supplies adequate precursors for flavonoid synthesis, potentially increasing the levels of flavonols, flavonoids, dihydroflavonols, chalcones, and dihydroflavones. The reactions of DFR and FLS utilize dihydroflavonol as a substrate to generate colored anthocyanins and colorless flavonols, with their levels being influenced by the expression of DFR and FLS . Findings suggest that competition between FLS and DFR for dihydromyricetin (DHM) may impede the production of delphinidin, thereby affecting the flavonol to anthocyanin ratio and leading to increased transcription of the OsFLS gene in rel1 -D. The high expression of genes such as OsPAL , OsC4H , Os4CL , OsCHS , OsCHI , OsF3H , and OsFLS was essential for the accumulation of flavonoid active components in rel1 -D. Furthermore, a Pearson correlation analysis was conducted to examine the relationship between the expression levels of these genes and the relative contents of flavonoids and flavonols, revealing that OsCHI , OsF3H , OsFLS , OsCHS , OsPAL , and Os4CL DEGs were critical for the accumulation of effective components in rel1 -D. Due to the strong connection between flavonoids and how plants adapt to their environment, most of the genes examined in our research exhibited tissue-specific expression patterns in rel1 -D mutant (Fig. 7 ). Some of these genes showed relatively high expression levels, indicating that various genes in the flavonoid biosynthesis pathway may serve distinct physiological roles in rice. The higher expression levels of OsANS, OsFLS1 , and OsCHI in the rel1 -D mutant suggest that these genes could be significant in the mutant's characteristics. These findings align with previous research conducted on other plant species [ 43 – 48 ]. Additionally, genes linked to flavonoid biosynthesis play a role in stress responses when plants face abiotic or biotic stresses throughout their life cycle [ 49 , 50 ], such as cold and salt stress [ 51 – 54 ]. In summary, the varying expression patterns of the genes involved in flavonoid scaffold biosynthesis observed in this study offer valuable insights for future functional identification. Multiple prior studies have indicated that enzymes involved in the flavonoid biosynthetic pathway often form complexes to facilitate metabolite channels [ 55 – 57 ]. A broad-target metabolome analysis identified a total of 793 metabolites, with 83 classified as flavonoids. This group included 43 flavonoids, 15 flavonols, 2 chalcones, 4 dihydroflavones, and 1 dihydroflavonol. These findings imply that flavonoid levels are particularly high in rel1 -D. The 23 flavonoids were classified into six categories based on their chemical structure: flavanones, flavonols, flavones, flavanols, isoflavones, and anthocyanins. The concentrations of chalcones, dihydroflavones, flavonoid carbonosides, flavonols, dihydroflavonols, and flavonoids were notably higher in rel1 -D. Additionally, the levels of quercetin-3-o-(2”-o-xylose), rutin, kaempferol-3-o-(6”-o-acetyl) glucoside, and kaempferol-3-o-(6”-malonyl) glucoside were significantly greater than those found in ZH11. The results indicated that the rel1 -D mutant had the ability to resist drought [ 37 ] and heat stress. Additionally, kaempferol and quercetin are two primary flavonoids that play a significant role in the plant's immune system [ 58 , 59 ]. Importantly, there is a close relationship between L-Phenylalanine and the metabolites kaempferol and quercetin, which suggests that L-phenylalanine is a key amino acid for drought resistance in rel1 -D. The rel1 -D mutant changed the expression of genes associated with flavonoid biosynthesis in rice, indicating that the genes and metabolites involved in flavonoid metabolism could enhance the biological characteristics of rel1 -D. In summary, the production of flavonoids may be boosted by directly influencing the expression of OsCHI , OsF3H , OsFLS , OsCHS , OsPAL , and Os4CL in the rel1 -D mutant, leading to an increase in the accumulation of active flavonoids. This research offers new insights for further exploration of the molecular mechanisms behind flavonol synthesis in rice. Conclusions Flavonoids are a diverse group of secondary metabolites derived from a variety of aglycones, which undergo important chemical modifications such as glycosylation and acylation [ 60 , 61 ]. In this paper, we summarize the development of flavonoid biosynthesis in rice. As noted above, we have improved our knowledge of the structural genes and chemistry of flavonoid biosynthesis by means of a variety of approaches, such as the analysis of natural mutant metabolomics and transcriptomic studies. Rice T-DNA insertion mutants and transcriptome sequencing data are valuable tools to identify the genes involved in flavonoid biosynthesis. Investigating the diversity and convergence within the flavonoid pathway provides insights into the chemistry of different species, paving the way for cross-species transformation. Because of the wide variety of flavonoid compounds found even within a single tissue, one of the most important areas of research is to identify which of these compounds play a significant role in tolerance to various stresses. Understanding their functions and their relative importance in organisms will be especially challenging. However, as flavonoids provide a wide range of protection benefits for both plants and animals, understanding the function of these diverse flavonoids is becoming more and more important. Materials & methods Materials and Stress Treatment of Plants Oryza sativa ZH11 (WT) was used as the wild type in the experiment, whereas the rel1 -D mutant was derived from our previous study [ 37 ]. All of the rice plants were cultivated in the paddy fields of Guangzhou, South China. In this study, all the seeds were newly harvested, and the seeds were collected from the individual plants to keep the uniformity. The seed dormancy was broken after they had been dried in an oven at 50°C for 5 days. In a completely randomized design, 6 replicates per control and treatment. The seedlings at 3-leaf stage were treated at 42°C for 48 hours. The survival rate of the seedlings was calculated when they returned to normal temperature (30 ℃) for 0, 2,4 and 6 days (the percentage of survival seedlings accounted for the total seedlings). Samples of leaves at the tillering stage were taken for histological examination. Plant tissue staining NBT was used to measure the production of superoxide anion in cells. In summary, 50 mg of NBT was added to 100 mL phosphate buffer (pH7.8) to complete dissolution. The NBT solution was obtained. The leaves were immersed in an NBT solution for 6 h. Plant seedlings or leaves were carefully removed with tweezers. The seedlings or leaves were dipped 3 to 5 times in distilled water. After drying on the filter paper, the excess water was immersed in 95% ethanol for 16 h at 40 ℃. The aim was to remove chlorophyll or light blue background from the plant seedlings or the leaves themselves. Fresh 95% ethanol was exchanged several times during processing. Cell death was measured by using the TAB. Briefly, leaves were placed in test tubes containing trypan blue staining solution (2.5 mg of trypan blue per ml, 25% [wt/v] lactic acid, 23% water-saturated phenol, 25% glycerol, and H 2 O), boiled for 10 min, and kept in the dark for 12 h. Next, the leaves were treated with 25 mg·mL − 1 chloral hydrate solution for 24 h to remove leaf color, and blue spots on the leaves were recorded and photographed. Transcriptome sequencing The complete transcriptome sequencing for this study was carried out by Wuhan Metwell Biotechnology Co., Ltd., with the cDNA library sequenced using the Illumina NOVA SEQ 6000 at Gene Denovo Biotechnology in Wuhan, China. The sequencing process utilized the Illumina TruseqTM RNA sample preparation kit for library construction. Total RNA was extracted from the samples using the TRIZOL kit (Invitrogen, Carlsbad, CA) following the manufacturer's instructions. The RNA's purity and concentration were evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA). For the construction of a single library, it was required that at least 1 µg of total RNA be provided, with a concentration no less than 35 ng·µL − 1 , an OD 260/280 ratio that should be 1.8 or higher, and an OD 260/230 ratio that should be 1.0 or higher. Additionally, the integrity of the RNA was verified through RNase-free agarose gel electrophoresis. The Illumina Novaseq 6000 system was created for sequencing short DNA fragments. The enriched mRNA consists of complete RNA sequences averaging several kilobases in length, were required to undergo random fragmentation. Random fragmentation of mRNA was achieved by introducing a fragmentation buffer, followed by the isolation of small fragments of approximately 300 base pairs through magnetic bead selection. The mRNA enrichment process was carried out using oligomeric (DT) beads from the Epicenter kit in Madison, Wisconsin. The intact mRNA fragments were then converted into shorter fragments and reverse transcribed into cDNA using Illumina's UltraRNA library preparation kit (NEB # 7530; New England Biolabs, Ipswich, MA). The purified double-stranded cDNA fragments were repaired and ligated to the Illumina sequencing linker. These ligated fragments were separated using agarose gel electrophoresis and Polymerase Chain Reaction (PCR). The double-stranded cDNA features sticky ends, which can be converted to blunt ends by adding a terminal repair mixture. An "A" base was subsequently added to the 3' end to facilitate the attachment of the Y-shaped linker. Finally, sequencing was conducted using the Illumina platform. Extraction, detection and Quantitative analysis of metabolites Sample processing A sample weighing 50 ± 5 mg was placed in a 2 mL centrifuge tube along with 6 mm diameter grinding beads. Then, 400 µL of an extract (methanol: water in a 4:1 ratio) containing 0.02 mg·mL − 1 of the internal standard (L-2-chlorophenylalanine) was added. The samples were ground for 6 minutes at -10℃ and 50 Hz using a frozen tissue grinder, followed by a 30 minutes ultrasonic extraction at 5℃ and 40 KHz. The samples were then stored at -20 ℃ for 30 minutes before being centrifuged for 15 minutes at 13,000 g and 4 ℃. The supernatant was collected and transferred to an injection vial for analysis. Additionally, 20 µL of supernatant from each sample was taken and combined to create a quality control sample. Quality control Quality Control (QC) samples were created by combining all sample extracts in equal volumes. Each QC sample was matched to the volume of the individual samples and underwent the same processing and testing procedures as the analytical samples. LC-MS testing The LC-MS analysis was conducted using the Thermo Fisher UHPLC-Q Exactive system. The chromatographic column utilized was the ACQUITY UPLC HSS T3 (100 mm × 2.1 mm i.d., 1.8 µm; Waters, Milford, USA). Mobile phase A consisted of 95% water and 5% acetonitrile (with 0.1% formic acid), while mobile phase B was made up of 47.5% acetonitrile, 47.5% isopropyl alcohol, and 5% water (also containing 0.1% formic acid). The flow rate was set at 0.40 mL·min − 1 , with an injection volume of 5 µL, and the column was maintained at a temperature of 40°C. Ionization of the sample was achieved through electrospray, and mass spectrometry signals were recorded in both positive and negative ion scanning modes. Validation of RNA-seq data by qRT-PCR The technique and calculation formula for qRT − PCR were outlined by Wu et al. [ 62 ]. The primers utilized are listed in Supplementary Table S8. Statistical analysis Gene abundance in DGA was measured using RSEM [ 63 ], and differential expression analysis was performed using DESeq2 [ 64 ]. In addition, a KEGG enrichment analysis was performed to determine which differentially expressed genes (DEGs) were significantly enriched in the KEGG pathway, with an adjusted p-value (Pajust) below 0.05. The Hierarchical Cluster Analysis (HCA) was performed with on-line software, with the default settings being available at https://cloud.metware.cn/toolCustom/3 . The Principal Component Analysis (PCA) and OPLS-DA were carried out with the help of the software available at https://www.omicshare.com/tools/Home/Soft/Become . In OriginPro 2021 (OriginLab, Northampton, MA, USA), a bar chart was produced. The HCA, PCA, and OPLS-DA data were standardized by Log10 transformation. Declarations Conflict of interest The authors declare no conflict of interest. Funding This work is supported by the Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (No. 2022SDZG05 to L.C.) and the Guangdong province rural revitalization strategy special fund seed industry revitalization project (2022-NJS-15-001). Author Contribution XW, LY and ZZ designed the project; XW,LY, JH, HL, GC, HW, XF, WZ, KL and ZZ performed the experiments; XW, LY, JH, HL, GC, HW, YL, XF and ZZ analyzed and interpreted the data; XW and ZZ wrote the paper.†Xiaojie Wu and Lingfang Yang contributed equally to this work. Acknowledgements This work was supported by the Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (No. 2022SDZG05 to L.C.) and the Guangdong province rural revitalization strategy special fund seed industry revitalization project (2022-NJS-15-001). References Mohammed AR, Tarpley L. High night temperature and plant growth regulator effects on spikelet sterility, grain characteristics and yield of rice (Oryza sativa L). Plants. 2011;91:283G291. Kong F, Jiu A, Kan Z, Zhou J, Yang H, Li FMJ. Deep tillage combined with straw biochar return increases rice yield by improving nitrogen availability and root distribution in the subsoil. Field Crops Res. 2024;315:109481. Yoo H, Chae HS, Kim YM, Kang M, Ryu KH, Ahn HC, Yoon KD, Chin YW, Kim JJB. Flavonoids and arylbenzofurans from the rhizomes and roots of Sophora tonkinensis with IL-6 production inhibitory activity. Bioorg Med Chem Lett. 2014;24(24):5644–7. Treutter DJ. Significance of Flavonoids in Plant Resistance and Enhancement of Their Biosynthesis. Plant Biol. 2005;7(06):581–91. Treutter DJ. Significance of flavonoids in plant resistance: a review. Environ Chem Lett. 2006;4(3):147–57. Weng JK. A functional and comparative study of the evolution of phenylpropanoid metabolism in land plants. Purdue Univ. 2009;4:7. Wen W, Alseekh S, Fernie ARJ. Conservation and diversification of flavonoid metabolism in the plant kingdom. Curr Opin Plant Biol. 2020;55:100–8. Lv YQ, Li D, Wu LY, Zhu YM, Ye Y, Zheng XQ, Ye JH. Sugar signal mediates flavonoid biosynthesis in tea leaves. Hortic Res. 2022; (1):3300–13. Ahmed FE, Mohamed T, Dina M, E-KE-NG, El GendyAbdelsamed I. ElshamyMohamed A.,Farag. proceedings of the Phytochemical Society of Europe: Natural acylated flavonoids: their chemistry and biological merits in context to molecular docking studies. Phytochemistry Reviews. 2023; 22(6):1469–1508. Yoo H, Chae HS, Kim YM, Kang M, Ryu KH, Ahn HC, Yoon KD, Chin YW, Kim JJB, Letters MC. Flavonoids and arylbenzofurans from the rhizomes and roots of Sophora tonkinensis with IL-6 production inhibitory activity. Bioorg Med Chem Lett. 2014;24(24):5644–7. Yu P, He X, Baer M, Beirinckx S, Hochholdinger FJNP. Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation. Nat Plants. 2021;7(4):481–99. Agati A, Pollastri SCITJP. Flavonoids as antioxidants in plants: Location and functional significance. Plant Sci. 2012;196:67–76. Shi B, Wu H, Zhu KQ. Minjie. Genome-Wide Identification and Expression Analysis of WRKY Genes during Anthocyanin Biosynthesis in the Mango (Mangifera indica L). Agriculture. 2022;12(6):821. Cavaiuolo M, Cocetta G, Ferrante AJA. The Antioxidants Changes in Ornamental Flowers during Development and Senescence. Antioxidants. 2013;2(3):132–55. Su L, Liu S, Liu X, Zhang B, Li L. Transcriptome profiling reveals histone deacetylase 1 gene overexpression improves flavonoid, isoflavonoid, and phenylpropanoid metabolism in Arachis hypogaea hairy roots. PeerJ Hubs. 2021;9:e10976. Li P, Li YJ, Zhang FJ, Zhang GZ, Jiang XY, Yu HM, Hou BK. The Arabidopsis UDP-glycosyltransferases UGT79B2 and UGT79B3, contribute to cold, salt and drought stress tolerance via modulating anthocyanin accumulation. Plant J. 2017;89(1):85–103. Nayeem S, Venkidasamy B, Sundararajan S, Kuppuraj SP, Ramalingam S. Differential expression of flavonoid biosynthesis genes and biochemical composition in different tissues of pigmented and non-pigmented rice. J Food Sci Technol. 2021;58(3):884–93. Ma D, Guo Y, Ali I, Lin J, Xu Y, Yang M. Accumulation characteristics of plant flavonoids and effects of cultivation measures on their biosynthesis: A review. Plant Physiol Biochem. 2024: 108960. Mbanjo EGN, Kretzschmar T, Jones H, Ereful N, Sreenivasulu N. The Genetic Basis and Nutritional Benefits of Pigmented Rice Grain. Front Genet. 2020;11:229. de Tohge LP, Fernie. Current understanding of the pathways of flavonoid biosynthesis in model and crop plants. J Exp Bot. 2017;68(15):4013–28. Zhang F, Guo H, Huang J, Yang C, Luo J. A UV-B-responsive glycosyltransferase, OsUGT706C2, modulates flavonoid metabolism in rice. Sci China Life Sci. 2020;63:1037–52. Du Y, Chu H, Lo C. CYP93G2 Is a Flavanone 2-Hydroxylase Required for C-Glycosylflavone Biosynthesis in Rice. Plant Physiol. 2010;154(1):324–33. Luang S, Cho JI, Mahong B, Opassiri R, Akiyama T, Phasai K, Komvongsa J, Sasaki N, Hua YL, Matsuba YJ. Rice Os9BGlu31 Is a Transglucosidase with the Capacity to Equilibrate Phenylpropanoid, Flavonoid, and Phytohormone Glycoconjugates. J Biol Chem. 2013;288(14):10111–23. Dong NQ, Lin HX. Contribution of phenylpropanoid metabolism to plant development and plant-environment interactions. J Integr Plant Biol. 2021;63(1):180–209. Barros J, Dixon RA. Plant Phenylalanine/Tyrosine Ammonia-lyases. Trends Plant Sci. 2020;25(1):66–79. Peng B, Zhang L, He S, Oerlemans R, Quax WJ, Groves MR, Haslinger K. Engineering a Plant Polyketide Synthase for the Biosynthesis of Methylated Flavonoids. J Agric Food Chem. 2023;72(1):529–39. Thomsen M, Tuukkanen A, Dickerhoff J, Palm GJ, Kratzat H, Svergun DI, Weisz K, Bornscheuer UT, Hinrichs W. Structure and catalytic mechanism of the evolutionarily unique bacterial chalcone isomerase. Acta Crystallographica Section D: Biological Crystallography. 2015; 71(4): 907–917. 2015. Lam PY, Zhu FY, Chan WL, Liu H, Lo C. Cytochrome P450 93G1 Is a Flavone Synthase II That Channels Flavanones to the Biosynthesis of Tricin O-Linked Conjugates in Rice. Plant Physiol. 2014;165(3):1315–27. Lam PY, Liu H, Lo C. Completion of Tricin Biosynthesis Pathway in Rice: Cytochrome P450 75B4 Is a Unique Chrysoeriol 5'-Hydroxylase. Plant Physiol. 2015;168(4):1527–36. Owens DK, Crosby KC, Runac J, Howard BA, Winkel BS. Biochemical and genetic characterization of Arabidopsis flavanone 3beta-hydroxylase. Plant Physiol Biochem. 2008;46(10):833–43. Stracke R, Vos RCHD, Bartelniewoehner L, Ishihara H, Sagasser M, Martens S, Weisshaar B. Metabolomic and genetic analyses of flavonol synthesis in Arabidopsis thaliana support the in vivo involvement of leucoanthocyanidin dioxygenase. Planta. 2009;229:427–45. Park S, Kim DH, Park BR, Lee JY, Lim SH. Molecular and Functional Characterization of Oryza sativa Flavonol Synthase (OsFLS), a Bifunctional Dioxygenase. J Agric Food Chem. 2019;67(26):7399–409. Mochamad AF, Taketo O, Mami Y, Aki HM, Yukiko N, Kensuke N, Shun I, Hiroki T, Md. AUA, Darusman LKJP, et al. KNApSAcK Family Databases: Integrated Metabolite–Plant Species Databases for Multifaceted Plant Research. Plant Cell Physiol. 2012;53(2):e1–1. Kawahara Y, Bastide MDL, Hamilton JP. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice. 2013;6(1):4. Hu L, He H, Zhu C, Peng X, Liu S. Genome-wide identification and phylogenetic analysis of the chalcone synthase gene family in rice. J Plant Res. 2017;130:95–105. Han Y, Cao Y, Jiang H, Ding TJMB. Genome-wide dissection of the chalcone synthase gene family in Oryza sativa. Mol Breeding. 2017;37:1–12. Chen Q, Qingjun W, Wenyi, Bohan, Haitao P, Haifeng Z. Characterization of Rolled and Erect Leaf 1 in regulating leave morphology in rice. J Exp Bot. 2015;66(19):6047–58. Zhang L, Sun Y, Ji J, Zhao W, Guo W, Li J, Bai Y, Wang D, Yan Z, Guo C. Flavonol synthase gene MsFLS13 regulates saline-alkali stress tolerance in alfalfa. Crop J. 2023;11(4):1218–29. Liu Y, Li Y, Liu Z, Wang L, Bi Z, Sun C, Yao P, Zhang J, Bai J, Zeng Y. Integrated transcriptomic and metabolomic analysis revealed altitude-related regulatory mechanisms on flavonoid accumulation in potato tubers. Food Res Int. 2023;170:112997. Yuan Y, Zuo J, Zhang H, Zu M, Yu M, Liu S. Transcriptome and metabolome profiling unveil the accumulation of flavonoids in Dendrobium officinale. Genomics. 2022;114(3):110324. Guo Y, Gao C, Wang M, Fu FF, El-Kassaby YA, Wang T, Wang G. Metabolome and transcriptome analyses reveal flavonoids biosynthesis differences in Ginkgo biloba associated with environmental conditions. Ind Crops Prod. 2020;158:112963. Dwivedi A, Singh V, Anwar K, Pareek A, Jain M. Integrated transcriptome, proteome and metabolome analyses revealed secondary metabolites and auxiliary carbohydrate metabolism augmenting drought tolerance in rice. Plant Physiol Biochem. 2023;201:107849. Luo X, Sun D, Wang S, Luo S, Fu Y, Niu L, Shi Q, Zhang Y. Integrating full-length transcriptomics and metabolomics reveals the regulatory mechanisms underlying yellow pigmentation in tree peony (Paeonia suffruticosa Andr.) flowers. Hortic Res. 2021; (001):008. Wanli X, Xuli P, Zange J, Zili X. Genome-wide Identification and Expression Analysis of Chalcone Synthase Gene in Pepper. Genomics and Applied Biology; 2015. Sangkyu P, Min C, Jong L, Jae K, Sun-Hwa H, Sun-Hyung. Molecular and Biochemical Analysis of Two Rice Flavonoid 3'-Hydroxylase to Evaluate Their Roles in Flavonoid Biosynthesis in Rice Grain. Int J Mol Sci. 2016;17(9):1549. Dong X, Chen W, Wang W, Zhang H, Liu X, Luo J. Comprehensive profiling and natural variation of flavonoids in rice. J Integr Plant Biol. 2014;56(9):876–86. Deng Y, Li C, Li H, Lu S. molecules identification and characterization of flavonoid biosynthetic enzyme genes in salvia miltiorrhiza (Lamiaceae). Molecules. 2018;23(6):1467. Anna B, David R. The Roles of a Flavone-6-Hydroxylase and 7-O-Demethylation in the Flavone Biosynthetic Network of Sweet Basil. J Biol Chem. 2013;288(3):1795–805. Dolferus R. To grow or not to grow: A stressful decision for plants - ScienceDirect. Plant Sci. 2014;229:247–61. Ahanger MA, Morad-Talab N, Abd‐Allah EF, Ahmad P, Hajiboland R. Plant growth under drought stress. Plant growth under drought stress: Significance of mineral nutrients. Water Stress Crop Plants: Sustainable Approach. 2016;2:649–68. Kim JH, Cheon YM, Kim BG, Ahn JH. Analysis of Flavonoids and Characterization of the OsFNS Gene Involved in Flavone Biosynthesis in Rice. J Plant Biology. 2008;51:97–101. Lee YJ, Kim JH, Kim BG, Lim Y, Ahn JH. Characterization of flavone synthase I from rice. BMB Rep. 2008;41(1):68–71. Cheng AX, Han XJ, Wu YF, Lou HX. The Function and Catalysis of 2-Oxoglutarate-Dependent Oxygenases Involved in Plant Flavonoid Biosynthesis. Int J Mol Sci. 2014;15(1):1080–95. Nan J, Andrea D, Erich G. Flavones: From Biosynthesis to Health Benefits. Plants 2016; 5(2). Martens S, Preu A, Matern U. Multifunctional flavonoid dioxygenases: flavonol and anthocyanin biosynthesis in Arabidopsis thaliana L. Phytochemistry. 2010;71(10):1040–9. Winkel, Brenda SJ. Metabolic channeling in plants. Annu Rev Plant Biol. 2004;55:85–107. Shih CH, Chu H, Tang LK, Sakamoto W, Lo C. Functional characterization of key structural genes in rice flavonoid biosynthesis. Planta. 2008;228:1043–54. Wang X, Radwan MM, Taráwneh AH, Gao J, Wedge DE, Rosa LH, Cutler HG, Cutler SJ. Antifungal activity against plant pathogens of metabolites from the endophytic fungus Cladosporium cladosporioides. J Agric Food Chem. 2013;61(19):4551–5. Zhang D, Wang S, Lin L, Zhang J, Cui M, Wang S, Zhao X, Dong J, Long Y, Xing Z. Integrative Analysis of Metabolome and Transcriptome Reveals the Mechanism of Flavonoid Biosynthesis in. ACS Omega. 2022;7(23):19437–53. Zhao J, Dixon RA. The 'ins' and 'outs' of flavonoid transport. Trends Plant Sci. 2010;15(2):72–80. Dixon RA. Engineering of plant natural product pathways. Curr Opin Plant Biol. 2005;8(3):329–36. Wu X, Chen B, Xiao J, Guo H. Different doses of UV-B radiation affect pigmented potatoes' growth and quality during the whole growth period. Front Plant Sci. 2023;14:1101172. Dewey CN, Li B. Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323–323. Love MI, Huber W, Anders SJ. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable..xlsx SupplementFig.docx Cite Share Download PDF Status: Published Journal Publication published 01 Sep, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 14 Nov, 2024 Editor assigned by journal 09 Nov, 2024 Submission checks completed at journal 09 Nov, 2024 First submitted to journal 07 Nov, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5406993","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378020530,"identity":"ed0f031f-72d5-4f36-b162-4f462b341646","order_by":0,"name":"Xiaojie Wu","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Wu","suffix":""},{"id":378020531,"identity":"6bdbea0b-6b4f-4a0b-b096-28615aded77b","order_by":1,"name":"Lingfang Yang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lingfang","middleName":"","lastName":"Yang","suffix":""},{"id":378020544,"identity":"902a5b7c-2377-4ca4-84e2-770e021862c1","order_by":2,"name":"Jinbo Han","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jinbo","middleName":"","lastName":"Han","suffix":""},{"id":378020546,"identity":"721fc03c-f83f-4156-882e-893121ca7313","order_by":3,"name":"Hanqing Liu","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hanqing","middleName":"","lastName":"Liu","suffix":""},{"id":378020547,"identity":"391ec361-219f-4c32-bb38-aa769bdcdffa","order_by":4,"name":"Gaokun Chen","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gaokun","middleName":"","lastName":"Chen","suffix":""},{"id":378020548,"identity":"03e638ed-1086-4c2f-aba3-b435ae5bef78","order_by":5,"name":"Haoyuan Wang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haoyuan","middleName":"","lastName":"Wang","suffix":""},{"id":378020550,"identity":"5d838c32-3308-4b1d-8344-749a8ffca75e","order_by":6,"name":"Xingru Feng","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xingru","middleName":"","lastName":"Feng","suffix":""},{"id":378020551,"identity":"0fcfc811-4165-458a-b7cd-07b4921fc4cf","order_by":7,"name":"Wan Zhang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"","lastName":"Zhang","suffix":""},{"id":378020552,"identity":"a97c5c55-bf94-49f6-ad54-d4fdd8d77349","order_by":8,"name":"Kangping Liu","email":"","orcid":"","institution":"Guangdong Yueliang seed industry Company Limited","correspondingAuthor":false,"prefix":"","firstName":"Kangping","middleName":"","lastName":"Liu","suffix":""},{"id":378020553,"identity":"5662dac0-4519-4355-b59e-d432fb124924","order_by":9,"name":"Zemin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACNhAhB+UwE6/FmHQtiQ1Ea5GfdvzZg587atPntx9+JsFQYZ3YwH72AF4tjLMT0g17zxzP3XAmzUyC4Ux6YgNPXgJeLczSCcckeNuO5W6QYDCTYGw7nNggwWOA3yPSiW2Sf9uOpcvPYP8mwfiPCC080sls0rxtNQkMN3iAtjQQoUVCOo1NWrbtgOGGMznFFgnH0o3beHLwa5Gfnf5M8m1bnbx8+/GNNz7UWMv2s5/BrwUKDkOoBAZoNBEB6ohUNwpGwSgYBSMSAABL4j91ukUqmAAAAABJRU5ErkJggg==","orcid":"","institution":"South China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zemin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-07 05:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5406993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5406993/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-11977-0","type":"published","date":"2025-09-01T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70027307,"identity":"eeed5189-81af-45a9-b863-0cf5ba1d9791","added_by":"auto","created_at":"2024-11-27 15:44:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437149,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypes of ZH11 and \u003cem\u003erel1\u003c/em\u003e-D mutants under high temperature treatment. (A), 30℃ is thenormal growth temperature, 42℃ represents high temperature treatment, and 0,2,4,6 days represent the time of returning to normal temperature after heat stress. (B), NBT and TAB staining were performed on the materials recovered on the 0 day.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/6cbe1a72413afb66f2c049d4.jpeg"},{"id":70026104,"identity":"0dc573f3-6a85-4132-92d6-b2ec803d0b55","added_by":"auto","created_at":"2024-11-27 15:36:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71756,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolites profile in wild and mutate. (A) Pearson correlation between WT and \u003cem\u003erel1\u003c/em\u003e-D repeats. (B) the PCA of the WT and \u003cem\u003erel1\u003c/em\u003e-D samples; and (C) the relative abundance of various metabolites. (D) The proportion of each subclass of flavonoid metabolites.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/ca750a7d5a6b5fee01d411a7.png"},{"id":70026095,"identity":"ec763c0f-a70a-4393-9d36-9965c366c326","added_by":"auto","created_at":"2024-11-27 15:36:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66859,"visible":true,"origin":"","legend":"\u003cp\u003eIntegration analysis of differential metabolites and differential genes.\u003cstrong\u003e \u003c/strong\u003e(A), volcano plot of DAMs. (B), Relative abundance of DAMs. (C), Differential metabolite classification plot. (D) Pathway enrichment analysis of DAMs and DEGs. (E) Correlation analysis of DAMs and DEGs.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/018f5a0a2641a545c3f8b96e.png"},{"id":70027306,"identity":"12cdfc8e-adf7-4912-b0b4-9f89e8c1beda","added_by":"auto","created_at":"2024-11-27 15:44:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73291,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome profiles of wild and mutated species. (A) PCA of transcriptome. (B) Volcano diagram of transcriptome. (C) Enrichment analysis for GO; and (D) Enrichment analysis for KEGG, in which the vertical axis indicates the KEGG pathways and the horizontal axis shows the Rich factor (higher Rich means more enrichment; the higher the score, the higher the number, the higher the difference, and the higher the concentration).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/f2ed8618b895718023f68df7.png"},{"id":70027308,"identity":"da760242-b5b1-459a-a133-0ef469f96be1","added_by":"auto","created_at":"2024-11-27 15:44:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12711,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR of several genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/e1cbf64a28b42eea9bf2499e.png"},{"id":70027309,"identity":"c82c7860-f97e-478b-8371-15f6e02bc7ae","added_by":"auto","created_at":"2024-11-27 15:44:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":232296,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential regulatory networks of genes and metabolites involved in flavonoid biosynthesis pathway, as well as various physiological characteristics. The coloured nodes represent the biosynthesis of flavonoids (blue), the metabolites of phenol (green) and the physiological properties (violet).\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/586e7e16b5f9da5eb5d350d1.png"},{"id":70026099,"identity":"bf03bdcc-599c-4ce7-aa5d-5d1bb706579c","added_by":"auto","created_at":"2024-11-27 15:36:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":90598,"visible":true,"origin":"","legend":"\u003cp\u003eGene, metabolite and transcription factor co-regulation analysis. (A), studying genes, metabolites and transcripition in the flavonoid pathway. Lines indicate enzymatic chemical reactions, circle nodes indicate metabolites, and the color of a solid (detected) or hollow (not detected) circle indicates the type of metabolite. \u003cem\u003ePAL\u003c/em\u003e, Phenylalanine ammonia-lyase, \u003cem\u003e4CL\u003c/em\u003e, 4-coumarate CoA ligase, \u003cem\u003eCHS\u003c/em\u003e, chalcone synthase, \u003cem\u003eCHI\u003c/em\u003e, chalcone isomerase, \u003cem\u003eF3H\u003c/em\u003e, flavanone 3-hydroxylase, \u003cem\u003eF3′H\u003c/em\u003e, flavonoid 3′-hydroxylase, \u003cem\u003eFLS\u003c/em\u003e, Flavonol synthase, \u003cem\u003eDFR\u003c/em\u003e, dihydroflavonol reductase, \u003cem\u003eANR\u003c/em\u003e, anthocyanidase, \u003cem\u003eLAR\u003c/em\u003e, Leucoanthocyanidin reductase. (B), OsHLH, OsMPS and OsCDC 20.2 protein interaction model predicted by AlphaFold3. (C), Cis-acting element in promoter of DEGs involed flavonoid biosynthesis. (D), The correlation of \u003cem\u003ePAL, C4H, 4CL, CHS, CHI, F3H, FLS\u003c/em\u003e and tflavanone, flavanone and flavonol (Pearson correlation coefficient ≥0.9, p value ≤0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/8e1d5c2aab446848b2d5b1cf.png"},{"id":90828161,"identity":"61821147-80c0-46f8-8a05-29b68fb78a3e","added_by":"auto","created_at":"2025-09-08 16:05:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2024334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/c400938b-ed3f-4066-b252-58ef6742b90a.pdf"},{"id":70026097,"identity":"b45f8d14-8c3c-4dac-8704-5d81b786bbf3","added_by":"auto","created_at":"2024-11-27 15:36:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":211194,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/7fd6592ad0797a4fe891da61.xlsx"},{"id":70026101,"identity":"75b47a5c-0b8e-49ce-bf16-870c2bde1153","added_by":"auto","created_at":"2024-11-27 15:36:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":813791,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementFig.docx","url":"https://assets-eu.researchsquare.com/files/rs-5406993/v1/79c4a55e4bc47052c9250568.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Analysis of Flavonoid Pathways in Rice: Enhancing Heat Tolerance","fulltext":[{"header":"Background","content":"\u003cp\u003eTemperature plays an important role in the development, yield and quality of rice. When the environmental temperature exceeds the critical value, the growth and development of rice are influenced. Statistical data shows that when the maximum daily temperature exceeds 38 ℃, it is known as high temperature, which is a threat to the development of rice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Flavones are important secondary metabolites of phenols. Rice (Oryza sativa) is one of the most important crops in the world [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A comprehensive understanding of biological processes such as plant development and stress resistance is critical for achieving high yield and quality in rice. Rice is known to contains a variety of flavonoids compounds, which are secondary metabolites, including chalcones, flavanoids, flavones, and anthocyanins [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recently, the stress resistance of flavonoid plants has been done of many researches, with an emphasis on their ability to resist high temperatures, drought, cold, and diseases being highlighted [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe production of flavonoids involves a multifaceted process known as the phenylpropionic acid pathway. It belongs to one of the largest families of secondary metabolites of polyphenols worldwide and is widely distributed in various plant organs and tissues [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The six primary classes of flavonoids are as follows: isoflavones, flavonoids, flavanones, flavonols, and anthocyanins [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Flavonoid, the most biologically active secondary metabolite of plant, has been extensively studied in recent years. It has been shown that these compounds play important roles in plant growth, development, reproduction, and stress tolerance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Flavonoids are antioxidants [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], scavenging reactive oxygen (Ros) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and they seem to be involved in protecting plants from infection, animal feeding, and stresses from both biotic and abiotic factors such as drought and thermal stress [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, flavonoids are known to possess a multitude of biological activities, including being antiviral, anti-cancer, anti-allergic, anti-inflammatory, and in the prevention of coronary heart disease, and they are endowed with significant nutritional and physiological benefits [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Thus, increasing the amounts of flavonoids is arguably more important for crop safety and human nutrition.\u003c/p\u003e \u003cp\u003eTo date, more than 9000 different flavonoid molecules have been discovered in many plant species [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Rice is the most economically important staple crop for human nutrition and serves as an experimental system for monocots [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast to dicotyledones such as Arabidopsis and Tomato [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the biosynthetic pathways of flavonoids in rice are relatively unknown. According to several pioneering studies [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], flavonoids are produced from L-Phenylalanine via the phenylpropionic acid pathway, while L-Phenylalanine is synthesized through the shikimic acid pathway [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The first three steps in the phenylpropanoid pathway are referred the general phenylpropanoid pathway [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, \u003cem\u003ePAL\u003c/em\u003e plays a key role in mediating carbon flux from primary to secondary metabolism in plants [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The first two processes in the flavonoid biosynthetic pathway are facilitated by chalcone synthase (\u003cem\u003eCHS\u003c/em\u003e) and Chalcone isomerase (\u003cem\u003eCHI\u003c/em\u003e) to form Chalcone [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and the sequential production of flavanones (including Naringin) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In rice, flavanones are converted to the corresponding flavones by \u003cem\u003eOsFNS I-1\u003c/em\u003e or \u003cem\u003eOsFNS II\u003c/em\u003e in vitro or in vivo [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Dihydrokaempferol is synthesized by flavanone 3-hydroxylase (\u003cem\u003eF3H\u003c/em\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The Flavonol synthase (\u003cem\u003eFLS\u003c/em\u003e) then converts the dihydroflavonols into flavonols by desaturation of the dihydroflavonols [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In Rice, \u003cem\u003eOsFLS\u003c/em\u003e is a bifunctional dioxygenase that exhibits \u003cem\u003eFLS\u003c/em\u003e and \u003cem\u003eF3H\u003c/em\u003e activity to exercise its function in flavonol production [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In typical cells, the flavonoid pathway can account for approximately 20% of the total carbon flux in plants [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The process of flavonoid metabolic diversity in plants plays an important role in plant adaptation to environmental diversity.\u003c/p\u003e \u003cp\u003eThe structural variety of flavonoids has a major role in determining their biological activity. The capacity to gather stocks of metabolites unique to a species has been rapidly enhanced with the advent of metabolomics [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Since the full genome sequencing of rice was completed in 2005, it has been established as a favored model organism in the field of functional genomics, attributed to its relatively compact genome size (~\u0026thinsp;389 Mb) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, there has not been a complete and systematic identification and investigation of the key structural genes involved in the biosynthesis of flavonoid scaffolds, except for those related to \u003cem\u003eCHS\u003c/em\u003e gene families [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the study will focus on rice, with the relative abundance of each flavonoid and the changes in the expression of key regulatory genes being systematically analyzed using transcriptomics and metabonomics. This study provides a theoretical basis for further studies on the mechanism of rice functional components and the improvement of rice quality.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSignificant morphological differences were observed between these two varieties. Compared with the wild type ZH11 (WT), \u003cem\u003erel1\u003c/em\u003e-D exhibited rolled leaves, a reduction in plant height, shorter ear length, fewer tillers and narrower leaf width. Additionally, the leaf roll index and leaf bending were significantly higher in \u003cem\u003erel1\u003c/em\u003e-D, while the maximal leaf width, 10-grain width, 10-grain length and 100-grain weight did not change significantly [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhenotype analysis of heat stress in rice seedlings\u003c/h2\u003e \u003cp\u003eThe regreening phenotype of ZH11 and \u003cem\u003erel1\u003c/em\u003e-D rice seedlings under high temperature (42 ℃) was shown in Fig.\u0026nbsp;1A. No significant difference in appearance was observed between ZH11 and \u003cem\u003erel1\u003c/em\u003e-D after high temperature treatment for 48 h. The survival of the \u003cem\u003erel1\u003c/em\u003e-D mutant leaf was observed to increase with the extention of recovery time, while ZH11 was dead on the 6th day (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), which indicated that \u003cem\u003erel1\u003c/em\u003e-D could tolerate high temperature and recover from heat damage.\u003c/p\u003e \u003cp\u003eThe content of superoxide anion in cells was determined by nitrotetrazolium blue chloride (NBT), and cell death was evaluated by using trypan blue staining (TAB). It was shown by NBT staining that superoxide anions had beed secreted in both ZH11 and \u003cem\u003erel1\u003c/em\u003e-D after 0 days of high temperature treatment (Fig.\u0026nbsp;1B), indicating that both of them were under stress. Almost all the cells in ZH11 were shown by TAB staining to died at 0 day after hyperthermia, but fewer in \u003cem\u003erel1\u003c/em\u003e-D (Fig.\u0026nbsp;1B). \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Phenotypes of ZH11 and \u003cem\u003erel1\u003c/em\u003e-D mutants under high temperature treatment. (A), 30℃ is thenormal growth temperature, 42℃ represents high temperature treatment, and 0,2,4,6 days represent the time of returning to normal temperature after heat stress. (B), NBT and TAB staining were performed on the materials recovered on the 0 day.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMetabolom analysis\u003c/h3\u003e\n\u003cp\u003eA total of 793 metabolites were identified across two groups using a targeted metabolomics approach. Excellent reproducibility was demonstrated by the analysis of three bioreplicates per sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In the main component analysis, the metabolites of 528 hybrid combinations were classified into PCA1 (41.7%) and PCA2 (20.27%), which were associated with a distinct spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Among them, 82 of them, 116, 46, 149, 30, 7, 66, and 73 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). It was found that flavonoids, lipids and phenols were the most abundant. The flavonoid category includes chalcones, dihydroflavone, dihydroflavonol, flavanols, flavonoids, flavonoid carbonoside, and flavonols, with flavonoids making up 47.65% of the total (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe metabolic changes of mutant \u003cem\u003erel1\u003c/em\u003e-D and ZH11(WT) were studied by metabonomics. In this paper, the differential accumulation of DAMs (DAMs) was investigated in the tillering period (Fig.\u0026nbsp;3A). A total of 126 differential metabolites were identified, of which 75 were upregulated and 51 were downregulated (Fig.\u0026nbsp;3B and Table S2). Co-association KEGG enrichment analysis revealed that \u003cem\u003erel1\u003c/em\u003e-D co-maps with ZH11(WT) in six pathways (Table S3), and interestingly, among these synthetic pathways, the flavonoid pathway and phenypropanoid biosynthesis were highly enriched (Fig.\u0026nbsp;3C, D). The results showed that threr was a positive correlation between these genes and the regulation of metabolites, suggesting that these genes might positively influence gene regulation (Fig.\u0026nbsp;3E). The highly correlated DAMs and DEGs were shown in the heat map. \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3.\u003c/b\u003e Integration analysis of differential metabolites and differential genes. (A), volcano plot of DAMs. (B), Relative abundance of DAMs. (C), Differential metabolite classification plot. (D) Pathway enrichment analysis of DAMs and DEGs. (E) Correlation analysis of DAMs and DEGs.\u003c/p\u003e\n\u003ch3\u003eThe difference in gene expression of the two materials\u003c/h3\u003e\n\u003cp\u003eTranscriptome sequencing was conducted on six samples, resulting in 39.04 GB of clean data, with each sample yielding over 6.14 GB. The Q30 rate was higher than 94.49% (Table S4). Each sample's clean reading was aligned with the specified reference genome, achieving an alignment ratio of 87.72\u0026ndash;96.27% (Table S5).\u003c/p\u003e \u003cp\u003eThe Cluster Analysis Heatmap showed that there was a strong bioreproducibility in all three varieties. In this study, 1184 DEGs were identified, of which 396 were up-regulated and 395 were down-regulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The GO annotation analysis showed 16 DEGs were classified into biological processes and molecular functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), with the majority of them being related to molecular and metabolic processes. Among the molecular processes, hydrolases were the most prevalent. The KEGG enrichment analysis showed that DEGs were significantly present in the pathways associated with the biosynthesis of flavonoids and the biosynthesis of secondary metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Table S6). These results suggest that the \u003cem\u003erel1\u003c/em\u003e-D mutant influences the flavonoid pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eqRT-PCR was used to validate the transcriptome data.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to validate the results of the RNA-Seq study, qRT-PCR was performed to confirm the results of the transcriptome. A selection of random genes, including \u003cem\u003eOsREL1\u003c/em\u003e, \u003cem\u003eOsIRL\u003c/em\u003e, \u003cem\u003eOsNAC4, OsROD1, OsCHS1, OsWAK14, OsWRKY67, OsPAL, OsCHIL2, Os4CL2\u003c/em\u003e, and \u003cem\u003eOsF3H2\u003c/em\u003e, were analyzed in the tillering leaves of ZH11 and \u003cem\u003erel1\u003c/em\u003e-D (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results showed that the RNA-Seq and qRT-PCR results did not differ significantly, which showed a similar pattern. As a result, the reliability of the RNA-Seq data used in this study was verified.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCoexpression networks reveal differential regulation of key genes, metabolites and physiological traits\u003c/h3\u003e\n\u003cp\u003eA co-expression network was constructed to explore the potential coexpression network of flavonoids, which included 581 genes and 126 differential transcription factors (PCCs). After the optimum parameter (β\u0026thinsp;=\u0026thinsp;5) was determined, the WGCNA algorithm (Fig. S2 A, B) was used to transform the correlation coefficients of each gene pair into neighboring coefficients. Subsequently, WGCNA was carried out to further correlate gene expression patterns with flavonoid biosynthesis and accumulation. The genes were separated into 11 different modules, and the Pearson correlation coefficient was used to determine the correlation between the genes of different modules. The darker the colour, the higher the correlation of the module is (Figure S2 C, Figure S3). Therefore, the dark module could be used to predict the coexpression network of flavonoids in rice.\u003c/p\u003e \u003cp\u003eIn order to know the regulatory network of the critical genes, metabolites and physiological forms of the mutants, 36 genes, 82 metabolites and 10 physiological characteristics were analyzed by Pearson (Table S7). Regulatory network pairs were visualized in cytoscape based on pearson correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.8 as a threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results indicated that there were 128 nodes connected to 3884 edges. 1,840 genes were found to show a negative correlation, and 2,44 were found to show a positive correlation. All in all, there was a strong positive correlation between the metabolism of flavonoids and phenols and the genes of the leaves.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDEGs and DAMs in the biosynthesis of flavonoids\u003c/h3\u003e\n\u003cp\u003eThrough enrichment of the KEGG pathway and gene ontology functional analysis, we identified 21 DEGs encoding flavonoid and phenolic acid biosynthetic enzymes, of which 1 was \u003cem\u003eOsPAL\u003c/em\u003e DEG, 1 was \u003cem\u003eOsC4H\u003c/em\u003e DEG, 2 were \u003cem\u003eOs4CL\u003c/em\u003e DEGs, 2 were \u003cem\u003eOsCHS\u003c/em\u003e DEGs, and 4 were \u003cem\u003eOsCHI\u003c/em\u003e DEGs, there were 3 \u003cem\u003eOsF3H\u003c/em\u003e DEGs, 1 \u003cem\u003eOsFLS\u003c/em\u003e DEG, 2 \u003cem\u003eOSUGT\u003c/em\u003e DEG, 1 \u003cem\u003eOsDFR\u003c/em\u003e DEG, 1 \u003cem\u003eOsANS\u003c/em\u003e DEG, 1 \u003cem\u003eOsLAR\u003c/em\u003e DEG, 2 \u003cem\u003eOSGT1\u003c/em\u003e DEGs and 1 \u003cem\u003eOsHIDH\u003c/em\u003e DEG. In order to investigate the mechanism of flavonoid accumulation in rice, we studied the gene expression patterns of both phenylpropane and flavonoids. The phenylpropanoid pathway provides precursors for flavonoid biosynthesis, including phenylalanine. The first two reactions of the flavonoid pathway were catalyzed by \u003cem\u003ePAL\u003c/em\u003e and \u003cem\u003eCHS\u003c/em\u003e, followed by diverse downstream reactions catalyzed by a series of enzymes, resulting in the aglycones biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The high expression of \u003cem\u003eOsPAL\u003c/em\u003e, \u003cem\u003eOsC4H\u003c/em\u003e, and \u003cem\u003eOs4CL\u003c/em\u003e produced enough precursors to synthesize flavonoids by utilizing \u003cem\u003eOs4CL\u003c/em\u003e to produce p-coumaroyl-CoA. This might be one of the reasons for higher proportions of flavanones, flavones, flavonols, isoflavones, and flavanols in \u003cem\u003erel1\u003c/em\u003e-D. Flavonoids were synthesized from naringenin, a substance catalyzed by \u003cem\u003eCHI\u003c/em\u003e. In this study, the increased expression of \u003cem\u003eOsCHI\u003c/em\u003e in \u003cem\u003erel1\u003c/em\u003e-D led to the highest accumulation of naringenin, with the downstream flavones, flavonols, isoflavones, and flavanols also exhibiting the highest accumulation in \u003cem\u003erel1\u003c/em\u003e-D, which could be attributed to the high expression of the \u003cem\u003eOsF3H\u003c/em\u003e genes in \u003cem\u003erel1\u003c/em\u003e-D.\u003c/p\u003e \u003cp\u003eThe transcription factors OsMYB1, OsMYB2, OsMYB4, OsMYB38, OsMYB102, OsHLH, OsMPS, OsDCD 20.2 were highly expressed in \u003cem\u003erel1\u003c/em\u003e-D. Predictions from the AlphaFold model (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://golgi.sandbox.google.com/\u003c/span\u003e\u003cspan address=\"https://golgi.sandbox.google.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) showed that the protein structures of transcription factors OsHLH, OsMPS and OsCDC 20.2 interacted (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among the promoters of \u003cem\u003eOsPAL, OsC4H, Os4CL, OsCHI, OsF3H, OsFLS, OsLAR, OsUGT, OsGT14, OsANS, OsDFR\u003c/em\u003e, etc., several binding original promoters of MYB, bHLH, WD40 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) were found, which showed that these genes and transcription factors regulated the synthesis of flavonoids in plants. The transcription level of \u003cem\u003eOsFLS\u003c/em\u003e was higher in \u003cem\u003erel1\u003c/em\u003e-D, and the contents of myricetin, quercetin, kaempferol, astragalin, quercitrin, rutin, iso\u0026shy;rhamnetin, quercetin 3-glucoside, and kaempferol 3-O-rutinoside in \u003cem\u003erel1\u003c/em\u003e-D were significantly higher than that in other stages. This result implied that the high expression of \u003cem\u003eOsPAL\u003c/em\u003e, \u003cem\u003eOsC4H\u003c/em\u003e, \u003cem\u003eOs4CL\u003c/em\u003e, \u003cem\u003eOsCHS\u003c/em\u003e, \u003cem\u003eOsCHI\u003c/em\u003e, \u003cem\u003eOsF3H\u003c/em\u003e, and \u003cem\u003eOsFLS\u003c/em\u003e were essential for the accumulation of effective components of flavonoids (flavanones, flavones, and flavonols) in \u003cem\u003erel1\u003c/em\u003e-D, which suggests that these genes are required for the biosynthesis of these compounds.\u003c/p\u003e \u003cp\u003eA Pearson\u0026rsquo;s correlation analysis was executed to examine the correlation between the expression levels of \u003cem\u003eOsPAL\u003c/em\u003e, \u003cem\u003eOsC4H\u003c/em\u003e, \u003cem\u003eOs4CL\u003c/em\u003e, \u003cem\u003eOsCHS\u003c/em\u003e, \u003cem\u003eOsCHI\u003c/em\u003e, \u003cem\u003eOsF3H\u003c/em\u003e, and \u003cem\u003eOsFLS\u003c/em\u003e of flavonoid biosynthesis pathway and relative contents of the 70 differentially expressed flavanones, flavones, and flavonols shown in Table S7 (Pearson\u0026rsquo;s correlation coefficient\u0026thinsp;\u0026ge;\u0026thinsp;0.8 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results showed that 4 DEGs for \u003cem\u003eOsCHI\u003c/em\u003e (Os03g0132900, Os10g0416500, Os12g0115700, Os11g0116300), 3 DEGs for \u003cem\u003eOsF3H\u003c/em\u003e (Os04g0376500, Os04g0667200, Os04g0662600), 1 DEG for \u003cem\u003eOsFLS\u003c/em\u003e (Os02g0767300), 1 DEG for \u003cem\u003eOsCHS\u003c/em\u003e (Os11g0530600), 1 DEG for \u003cem\u003eOsPAL\u003c/em\u003e (Os02g0626100), 2 DEGs for \u003cem\u003eOs4CL\u003c/em\u003e (Os02g0697400, Os02g0177600) were significantly correlated with dihydroquercetin, hyperoside, kaempferol, kaempferol-3-O-rutinoside, luteolin, luteoloside, myricetin, naringenin, nobiletin, quercetin, quercetin 3-glucoside, rutin, and tiliroside (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). This result suggested that these 9 DEGs were the key contributors to the accumulation of effective components in \u003cem\u003erel1\u003c/em\u003e-D.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt was found that the \u003cem\u003erel1\u003c/em\u003e-D mutant had a higher ratio of green and survival than ZH11(Fig.\u0026nbsp;1, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), RNA-seq and metabolome were used to analyze two rice varieties at tillering stage. To study the molecular mechanism of rice resistance to high temperature can improve the understanding of rice regulation mechanism under abiotic stress, and provide theoretical basis for the subsequent breeding and screening of high temperature tolerant varieties.\u003c/p\u003e \u003cp\u003eFlavonoids are one of the most important groups of secondary metabolites, which play a key role in plant biology, including resistance to pathogenic agents, growth regulation, and root development [\u003cspan additionalcitationids=\"CR39 CR40 CR41\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Several enzymes involved in flavonoid biosynthesis have been identified, such as \u003cem\u003eOsPAL\u003c/em\u003e DEG, \u003cem\u003eOsC4H\u003c/em\u003e DEG, \u003cem\u003eOs4CL\u003c/em\u003e DEG, \u003cem\u003eOsCHS\u003c/em\u003e DEG, \u003cem\u003eOsCHI\u003c/em\u003e DEG, \u003cem\u003eOsF3H\u003c/em\u003e DEG, \u003cem\u003eOsFLS\u003c/em\u003e DEG, \u003cem\u003eOsUGT\u003c/em\u003e DEG, \u003cem\u003eOsDFR\u003c/em\u003e DEG, \u003cem\u003eOsANS\u003c/em\u003e DEG, \u003cem\u003eOsLAR\u003c/em\u003e DEG, \u003cem\u003eOsGT1\u003c/em\u003e DEG, and \u003cem\u003eOsHIDH\u003c/em\u003e DEG. Within the flavonoid biosynthesis pathway, \u003cem\u003eCHS\u003c/em\u003e facilitates the isomerization of important intermediates into flavones and flavonols [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The elevated expression of \u003cem\u003eOsPAL\u003c/em\u003e, \u003cem\u003eOsC4H\u003c/em\u003e, and \u003cem\u003eOs4CL\u003c/em\u003e in \u003cem\u003erel1\u003c/em\u003e-D leads to the production of p-coumaryl-CoA, which supplies adequate precursors for flavonoid synthesis, potentially increasing the levels of flavonols, flavonoids, dihydroflavonols, chalcones, and dihydroflavones. The reactions of \u003cem\u003eDFR\u003c/em\u003e and \u003cem\u003eFLS\u003c/em\u003e utilize dihydroflavonol as a substrate to generate colored anthocyanins and colorless flavonols, with their levels being influenced by the expression of \u003cem\u003eDFR\u003c/em\u003e and \u003cem\u003eFLS\u003c/em\u003e. Findings suggest that competition between \u003cem\u003eFLS\u003c/em\u003e and \u003cem\u003eDFR\u003c/em\u003e for dihydromyricetin (DHM) may impede the production of delphinidin, thereby affecting the flavonol to anthocyanin ratio and leading to increased transcription of the \u003cem\u003eOsFLS\u003c/em\u003e gene in \u003cem\u003erel1\u003c/em\u003e-D. The high expression of genes such as \u003cem\u003eOsPAL\u003c/em\u003e, \u003cem\u003eOsC4H\u003c/em\u003e, \u003cem\u003eOs4CL\u003c/em\u003e, \u003cem\u003eOsCHS\u003c/em\u003e, \u003cem\u003eOsCHI\u003c/em\u003e, \u003cem\u003eOsF3H\u003c/em\u003e, and \u003cem\u003eOsFLS\u003c/em\u003e was essential for the accumulation of flavonoid active components in \u003cem\u003erel1\u003c/em\u003e-D. Furthermore, a Pearson correlation analysis was conducted to examine the relationship between the expression levels of these genes and the relative contents of flavonoids and flavonols, revealing that \u003cem\u003eOsCHI\u003c/em\u003e, \u003cem\u003eOsF3H\u003c/em\u003e, \u003cem\u003eOsFLS\u003c/em\u003e, \u003cem\u003eOsCHS\u003c/em\u003e, \u003cem\u003eOsPAL\u003c/em\u003e, and \u003cem\u003eOs4CL\u003c/em\u003e DEGs were critical for the accumulation of effective components in \u003cem\u003erel1\u003c/em\u003e-D.\u003c/p\u003e \u003cp\u003eDue to the strong connection between flavonoids and how plants adapt to their environment, most of the genes examined in our research exhibited tissue-specific expression patterns in \u003cem\u003erel1\u003c/em\u003e-D mutant (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Some of these genes showed relatively high expression levels, indicating that various genes in the flavonoid biosynthesis pathway may serve distinct physiological roles in rice. The higher expression levels of \u003cem\u003eOsANS, OsFLS1\u003c/em\u003e, and \u003cem\u003eOsCHI\u003c/em\u003e in the \u003cem\u003erel1\u003c/em\u003e-D mutant suggest that these genes could be significant in the mutant's characteristics. These findings align with previous research conducted on other plant species [\u003cspan additionalcitationids=\"CR44 CR45 CR46 CR47\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Additionally, genes linked to flavonoid biosynthesis play a role in stress responses when plants face abiotic or biotic stresses throughout their life cycle [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], such as cold and salt stress [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In summary, the varying expression patterns of the genes involved in flavonoid scaffold biosynthesis observed in this study offer valuable insights for future functional identification.\u003c/p\u003e \u003cp\u003eMultiple prior studies have indicated that enzymes involved in the flavonoid biosynthetic pathway often form complexes to facilitate metabolite channels [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. A broad-target metabolome analysis identified a total of 793 metabolites, with 83 classified as flavonoids. This group included 43 flavonoids, 15 flavonols, 2 chalcones, 4 dihydroflavones, and 1 dihydroflavonol. These findings imply that flavonoid levels are particularly high in \u003cem\u003erel1\u003c/em\u003e-D. The 23 flavonoids were classified into six categories based on their chemical structure: flavanones, flavonols, flavones, flavanols, isoflavones, and anthocyanins. The concentrations of chalcones, dihydroflavones, flavonoid carbonosides, flavonols, dihydroflavonols, and flavonoids were notably higher in \u003cem\u003erel1\u003c/em\u003e-D. Additionally, the levels of quercetin-3-o-(2\u0026rdquo;-o-xylose), rutin, kaempferol-3-o-(6\u0026rdquo;-o-acetyl) glucoside, and kaempferol-3-o-(6\u0026rdquo;-malonyl) glucoside were significantly greater than those found in ZH11.\u003c/p\u003e \u003cp\u003eThe results indicated that the \u003cem\u003erel1\u003c/em\u003e-D mutant had the ability to resist drought [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and heat stress. Additionally, kaempferol and quercetin are two primary flavonoids that play a significant role in the plant's immune system [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Importantly, there is a close relationship between L-Phenylalanine and the metabolites kaempferol and quercetin, which suggests that L-phenylalanine is a key amino acid for drought resistance in \u003cem\u003erel1\u003c/em\u003e-D.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003erel1\u003c/em\u003e-D mutant changed the expression of genes associated with flavonoid biosynthesis in rice, indicating that the genes and metabolites involved in flavonoid metabolism could enhance the biological characteristics of \u003cem\u003erel1\u003c/em\u003e-D. In summary, the production of flavonoids may be boosted by directly influencing the expression of \u003cem\u003eOsCHI\u003c/em\u003e, \u003cem\u003eOsF3H\u003c/em\u003e, \u003cem\u003eOsFLS\u003c/em\u003e, \u003cem\u003eOsCHS\u003c/em\u003e, \u003cem\u003eOsPAL\u003c/em\u003e, and \u003cem\u003eOs4CL\u003c/em\u003e in the \u003cem\u003erel1\u003c/em\u003e-D mutant, leading to an increase in the accumulation of active flavonoids. This research offers new insights for further exploration of the molecular mechanisms behind flavonol synthesis in rice.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFlavonoids are a diverse group of secondary metabolites derived from a variety of aglycones, which undergo important chemical modifications such as glycosylation and acylation [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In this paper, we summarize the development of flavonoid biosynthesis in rice. As noted above, we have improved our knowledge of the structural genes and chemistry of flavonoid biosynthesis by means of a variety of approaches, such as the analysis of natural mutant metabolomics and transcriptomic studies. Rice T-DNA insertion mutants and transcriptome sequencing data are valuable tools to identify the genes involved in flavonoid biosynthesis.\u003c/p\u003e \u003cp\u003eInvestigating the diversity and convergence within the flavonoid pathway provides insights into the chemistry of different species, paving the way for cross-species transformation. Because of the wide variety of flavonoid compounds found even within a single tissue, one of the most important areas of research is to identify which of these compounds play a significant role in tolerance to various stresses. Understanding their functions and their relative importance in organisms will be especially challenging. However, as flavonoids provide a wide range of protection benefits for both plants and animals, understanding the function of these diverse flavonoids is becoming more and more important.\u003c/p\u003e"},{"header":"Materials \u0026 methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMaterials and Stress Treatment of Plants\u003c/h2\u003e \u003cp\u003eOryza sativa ZH11 (WT) was used as the wild type in the experiment, whereas the \u003cem\u003erel1\u003c/em\u003e-D mutant was derived from our previous study [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All of the rice plants were cultivated in the paddy fields of Guangzhou, South China. In this study, all the seeds were newly harvested, and the seeds were collected from the individual plants to keep the uniformity. The seed dormancy was broken after they had been dried in an oven at 50\u0026deg;C for 5 days. In a completely randomized design, 6 replicates per control and treatment. The seedlings at 3-leaf stage were treated at 42\u0026deg;C for 48 hours. The survival rate of the seedlings was calculated when they returned to normal temperature (30 ℃) for 0, 2,4 and 6 days (the percentage of survival seedlings accounted for the total seedlings). Samples of leaves at the tillering stage were taken for histological examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePlant tissue staining\u003c/h2\u003e \u003cp\u003eNBT was used to measure the production of superoxide anion in cells. In summary, 50 mg of NBT was added to 100 mL phosphate buffer (pH7.8) to complete dissolution. The NBT solution was obtained. The leaves were immersed in an NBT solution for 6 h. Plant seedlings or leaves were carefully removed with tweezers. The seedlings or leaves were dipped 3 to 5 times in distilled water. After drying on the filter paper, the excess water was immersed in 95% ethanol for 16 h at 40 ℃. The aim was to remove chlorophyll or light blue background from the plant seedlings or the leaves themselves. Fresh 95% ethanol was exchanged several times during processing.\u003c/p\u003e \u003cp\u003eCell death was measured by using the TAB. Briefly, leaves were placed in test tubes containing trypan blue staining solution (2.5 mg of trypan blue per ml, 25% [wt/v] lactic acid, 23% water-saturated phenol, 25% glycerol, and H\u003csub\u003e2\u003c/sub\u003eO), boiled for 10 min, and kept in the dark for 12 h. Next, the leaves were treated with 25 mg\u0026middot;mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e chloral hydrate solution for 24 h to remove leaf color, and blue spots on the leaves were recorded and photographed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome sequencing\u003c/h2\u003e \u003cp\u003eThe complete transcriptome sequencing for this study was carried out by Wuhan Metwell Biotechnology Co., Ltd., with the cDNA library sequenced using the Illumina NOVA SEQ 6000 at Gene Denovo Biotechnology in Wuhan, China. The sequencing process utilized the Illumina TruseqTM RNA sample preparation kit for library construction. Total RNA was extracted from the samples using the TRIZOL kit (Invitrogen, Carlsbad, CA) following the manufacturer's instructions. The RNA's purity and concentration were evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA). For the construction of a single library, it was required that at least 1 \u0026micro;g of total RNA be provided, with a concentration no less than 35 ng\u0026middot;\u0026micro;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, an OD 260/280 ratio that should be 1.8 or higher, and an OD 260/230 ratio that should be 1.0 or higher. Additionally, the integrity of the RNA was verified through RNase-free agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003eThe Illumina Novaseq 6000 system was created for sequencing short DNA fragments. The enriched mRNA consists of complete RNA sequences averaging several kilobases in length, were required to undergo random fragmentation. Random fragmentation of mRNA was achieved by introducing a fragmentation buffer, followed by the isolation of small fragments of approximately 300 base pairs through magnetic bead selection. The mRNA enrichment process was carried out using oligomeric (DT) beads from the Epicenter kit in Madison, Wisconsin. The intact mRNA fragments were then converted into shorter fragments and reverse transcribed into cDNA using Illumina's UltraRNA library preparation kit (NEB # 7530; New England Biolabs, Ipswich, MA). The purified double-stranded cDNA fragments were repaired and ligated to the Illumina sequencing linker. These ligated fragments were separated using agarose gel electrophoresis and Polymerase Chain Reaction (PCR). The double-stranded cDNA features sticky ends, which can be converted to blunt ends by adding a terminal repair mixture. An \"A\" base was subsequently added to the 3' end to facilitate the attachment of the Y-shaped linker. Finally, sequencing was conducted using the Illumina platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExtraction, detection and Quantitative analysis of metabolites\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eSample processing\u003c/h2\u003e \u003cp\u003eA sample weighing 50\u0026thinsp;\u0026plusmn;\u0026thinsp;5 mg was placed in a 2 mL centrifuge tube along with 6 mm diameter grinding beads. Then, 400 \u0026micro;L of an extract (methanol: water in a 4:1 ratio) containing 0.02 mg\u0026middot;mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of the internal standard (L-2-chlorophenylalanine) was added. The samples were ground for 6 minutes at -10℃ and 50 Hz using a frozen tissue grinder, followed by a 30 minutes ultrasonic extraction at 5℃ and 40 KHz. The samples were then stored at -20 ℃ for 30 minutes before being centrifuged for 15 minutes at 13,000 g and 4 ℃. The supernatant was collected and transferred to an injection vial for analysis. Additionally, 20 \u0026micro;L of supernatant from each sample was taken and combined to create a quality control sample.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuality control\u003c/h2\u003e \u003cp\u003eQuality Control (QC) samples were created by combining all sample extracts in equal volumes. Each QC sample was matched to the volume of the individual samples and underwent the same processing and testing procedures as the analytical samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS testing\u003c/h2\u003e \u003cp\u003eThe LC-MS analysis was conducted using the Thermo Fisher UHPLC-Q Exactive system. The chromatographic column utilized was the ACQUITY UPLC HSS T3 (100 mm \u0026times; 2.1 mm i.d., 1.8 \u0026micro;m; Waters, Milford, USA). Mobile phase A consisted of 95% water and 5% acetonitrile (with 0.1% formic acid), while mobile phase B was made up of 47.5% acetonitrile, 47.5% isopropyl alcohol, and 5% water (also containing 0.1% formic acid). The flow rate was set at 0.40 mL\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an injection volume of 5 \u0026micro;L, and the column was maintained at a temperature of 40\u0026deg;C. Ionization of the sample was achieved through electrospray, and mass spectrometry signals were recorded in both positive and negative ion scanning modes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eValidation of RNA-seq data by qRT-PCR\u003c/h2\u003e \u003cp\u003eThe technique and calculation formula for qRT\u0026thinsp;\u0026minus;\u0026thinsp;PCR were outlined by Wu et al. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The primers utilized are listed in Supplementary Table S8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eGene abundance in DGA was measured using RSEM [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and differential expression analysis was performed using DESeq2 [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In addition, a KEGG enrichment analysis was performed to determine which differentially expressed genes (DEGs) were significantly enriched in the KEGG pathway, with an adjusted p-value (Pajust) below 0.05. The Hierarchical Cluster Analysis (HCA) was performed with on-line software, with the default settings being available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cloud.metware.cn/toolCustom/3\u003c/span\u003e\u003cspan address=\"https://cloud.metware.cn/toolCustom/3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The Principal Component Analysis (PCA) and OPLS-DA were carried out with the help of the software available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicshare.com/tools/Home/Soft/Become\u003c/span\u003e\u003cspan address=\"https://www.omicshare.com/tools/Home/Soft/Become\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. In OriginPro 2021 (OriginLab, Northampton, MA, USA), a bar chart was produced. The HCA, PCA, and OPLS-DA data were standardized by Log10 transformation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work is supported by the Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (No. 2022SDZG05 to L.C.) and the Guangdong province rural revitalization strategy special fund seed industry revitalization project (2022-NJS-15-001).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXW, LY and ZZ designed the project; XW,LY, JH, HL, GC, HW, XF, WZ, KL and ZZ performed the experiments; XW, LY, JH, HL, GC, HW, YL, XF and ZZ analyzed and interpreted the data; XW and ZZ wrote the paper.\u0026dagger;Xiaojie Wu and Lingfang Yang contributed equally to this work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Open Competition Program of the Top Ten Critical Priorities of Agricultural Science and Technology Innovation for the 14th Five-Year Plan of Guangdong Province (No. 2022SDZG05 to L.C.) and the Guangdong province rural revitalization strategy special fund seed industry revitalization project (2022-NJS-15-001).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMohammed AR, Tarpley L. High night temperature and plant growth regulator effects on spikelet sterility, grain characteristics and yield of rice (Oryza sativa L). Plants. 2011;91:283G291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong F, Jiu A, Kan Z, Zhou J, Yang H, Li FMJ. Deep tillage combined with straw biochar return increases rice yield by improving nitrogen availability and root distribution in the subsoil. Field Crops Res. 2024;315:109481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo H, Chae HS, Kim YM, Kang M, Ryu KH, Ahn HC, Yoon KD, Chin YW, Kim JJB. Flavonoids and arylbenzofurans from the rhizomes and roots of Sophora tonkinensis with IL-6 production inhibitory activity. Bioorg Med Chem Lett. 2014;24(24):5644\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTreutter DJ. Significance of Flavonoids in Plant Resistance and Enhancement of Their Biosynthesis. Plant Biol. 2005;7(06):581\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTreutter DJ. Significance of flavonoids in plant resistance: a review. Environ Chem Lett. 2006;4(3):147\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng JK. A functional and comparative study of the evolution of phenylpropanoid metabolism in land plants. Purdue Univ. 2009;4:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen W, Alseekh S, Fernie ARJ. Conservation and diversification of flavonoid metabolism in the plant kingdom. Curr Opin Plant Biol. 2020;55:100\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv YQ, Li D, Wu LY, Zhu YM, Ye Y, Zheng XQ, Ye JH. Sugar signal mediates flavonoid biosynthesis in tea leaves. Hortic Res. 2022; (1):3300\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed FE, Mohamed T, Dina M, E-KE-NG, El GendyAbdelsamed I. ElshamyMohamed A.,Farag. proceedings of the Phytochemical Society of Europe: Natural acylated flavonoids: their chemistry and biological merits in context to molecular docking studies. Phytochemistry Reviews. 2023; 22(6):1469\u0026ndash;1508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoo H, Chae HS, Kim YM, Kang M, Ryu KH, Ahn HC, Yoon KD, Chin YW, Kim JJB, Letters MC. Flavonoids and arylbenzofurans from the rhizomes and roots of Sophora tonkinensis with IL-6 production inhibitory activity. Bioorg Med Chem Lett. 2014;24(24):5644\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu P, He X, Baer M, Beirinckx S, Hochholdinger FJNP. Plant flavones enrich rhizosphere Oxalobacteraceae to improve maize performance under nitrogen deprivation. Nat Plants. 2021;7(4):481\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgati A, Pollastri SCITJP. Flavonoids as antioxidants in plants: Location and functional significance. Plant Sci. 2012;196:67\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi B, Wu H, Zhu KQ. Minjie. Genome-Wide Identification and Expression Analysis of WRKY Genes during Anthocyanin Biosynthesis in the Mango (Mangifera indica L). Agriculture. 2022;12(6):821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCavaiuolo M, Cocetta G, Ferrante AJA. The Antioxidants Changes in Ornamental Flowers during Development and Senescence. Antioxidants. 2013;2(3):132\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu L, Liu S, Liu X, Zhang B, Li L. Transcriptome profiling reveals histone deacetylase 1 gene overexpression improves flavonoid, isoflavonoid, and phenylpropanoid metabolism in Arachis hypogaea hairy roots. PeerJ Hubs. 2021;9:e10976.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi P, Li YJ, Zhang FJ, Zhang GZ, Jiang XY, Yu HM, Hou BK. The Arabidopsis UDP-glycosyltransferases UGT79B2 and UGT79B3, contribute to cold, salt and drought stress tolerance via modulating anthocyanin accumulation. Plant J. 2017;89(1):85\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayeem S, Venkidasamy B, Sundararajan S, Kuppuraj SP, Ramalingam S. Differential expression of flavonoid biosynthesis genes and biochemical composition in different tissues of pigmented and non-pigmented rice. J Food Sci Technol. 2021;58(3):884\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa D, Guo Y, Ali I, Lin J, Xu Y, Yang M. Accumulation characteristics of plant flavonoids and effects of cultivation measures on their biosynthesis: A review. Plant Physiol Biochem. 2024: 108960.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMbanjo EGN, Kretzschmar T, Jones H, Ereful N, Sreenivasulu N. The Genetic Basis and Nutritional Benefits of Pigmented Rice Grain. Front Genet. 2020;11:229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Tohge LP, Fernie. Current understanding of the pathways of flavonoid biosynthesis in model and crop plants. J Exp Bot. 2017;68(15):4013\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang F, Guo H, Huang J, Yang C, Luo J. A UV-B-responsive glycosyltransferase, OsUGT706C2, modulates flavonoid metabolism in rice. Sci China Life Sci. 2020;63:1037\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu Y, Chu H, Lo C. CYP93G2 Is a Flavanone 2-Hydroxylase Required for C-Glycosylflavone Biosynthesis in Rice. Plant Physiol. 2010;154(1):324\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuang S, Cho JI, Mahong B, Opassiri R, Akiyama T, Phasai K, Komvongsa J, Sasaki N, Hua YL, Matsuba YJ. Rice Os9BGlu31 Is a Transglucosidase with the Capacity to Equilibrate Phenylpropanoid, Flavonoid, and Phytohormone Glycoconjugates. J Biol Chem. 2013;288(14):10111\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong NQ, Lin HX. Contribution of phenylpropanoid metabolism to plant development and plant-environment interactions. J Integr Plant Biol. 2021;63(1):180\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarros J, Dixon RA. Plant Phenylalanine/Tyrosine Ammonia-lyases. Trends Plant Sci. 2020;25(1):66\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng B, Zhang L, He S, Oerlemans R, Quax WJ, Groves MR, Haslinger K. Engineering a Plant Polyketide Synthase for the Biosynthesis of Methylated Flavonoids. J Agric Food Chem. 2023;72(1):529\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomsen M, Tuukkanen A, Dickerhoff J, Palm GJ, Kratzat H, Svergun DI, Weisz K, Bornscheuer UT, Hinrichs W. Structure and catalytic mechanism of the evolutionarily unique bacterial chalcone isomerase. Acta Crystallographica Section D: Biological Crystallography. 2015; 71(4): 907\u0026ndash;917. 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam PY, Zhu FY, Chan WL, Liu H, Lo C. Cytochrome P450 93G1 Is a Flavone Synthase II That Channels Flavanones to the Biosynthesis of Tricin O-Linked Conjugates in Rice. Plant Physiol. 2014;165(3):1315\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam PY, Liu H, Lo C. Completion of Tricin Biosynthesis Pathway in Rice: Cytochrome P450 75B4 Is a Unique Chrysoeriol 5'-Hydroxylase. Plant Physiol. 2015;168(4):1527\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens DK, Crosby KC, Runac J, Howard BA, Winkel BS. Biochemical and genetic characterization of Arabidopsis flavanone 3beta-hydroxylase. Plant Physiol Biochem. 2008;46(10):833\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStracke R, Vos RCHD, Bartelniewoehner L, Ishihara H, Sagasser M, Martens S, Weisshaar B. Metabolomic and genetic analyses of flavonol synthesis in Arabidopsis thaliana support the in vivo involvement of leucoanthocyanidin dioxygenase. Planta. 2009;229:427\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark S, Kim DH, Park BR, Lee JY, Lim SH. Molecular and Functional Characterization of Oryza sativa Flavonol Synthase (OsFLS), a Bifunctional Dioxygenase. J Agric Food Chem. 2019;67(26):7399\u0026ndash;409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMochamad AF, Taketo O, Mami Y, Aki HM, Yukiko N, Kensuke N, Shun I, Hiroki T, Md. AUA, Darusman LKJP, et al. KNApSAcK Family Databases: Integrated Metabolite\u0026ndash;Plant Species Databases for Multifaceted Plant Research. Plant Cell Physiol. 2012;53(2):e1\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawahara Y, Bastide MDL, Hamilton JP. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice. 2013;6(1):4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu L, He H, Zhu C, Peng X, Liu S. Genome-wide identification and phylogenetic analysis of the chalcone synthase gene family in rice. J Plant Res. 2017;130:95\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan Y, Cao Y, Jiang H, Ding TJMB. Genome-wide dissection of the chalcone synthase gene family in Oryza sativa. Mol Breeding. 2017;37:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Q, Qingjun W, Wenyi, Bohan, Haitao P, Haifeng Z. Characterization of Rolled and Erect Leaf 1 in regulating leave morphology in rice. J Exp Bot. 2015;66(19):6047\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Sun Y, Ji J, Zhao W, Guo W, Li J, Bai Y, Wang D, Yan Z, Guo C. Flavonol synthase gene MsFLS13 regulates saline-alkali stress tolerance in alfalfa. Crop J. 2023;11(4):1218\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Li Y, Liu Z, Wang L, Bi Z, Sun C, Yao P, Zhang J, Bai J, Zeng Y. Integrated transcriptomic and metabolomic analysis revealed altitude-related regulatory mechanisms on flavonoid accumulation in potato tubers. Food Res Int. 2023;170:112997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan Y, Zuo J, Zhang H, Zu M, Yu M, Liu S. Transcriptome and metabolome profiling unveil the accumulation of flavonoids in Dendrobium officinale. Genomics. 2022;114(3):110324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Gao C, Wang M, Fu FF, El-Kassaby YA, Wang T, Wang G. Metabolome and transcriptome analyses reveal flavonoids biosynthesis differences in Ginkgo biloba associated with environmental conditions. Ind Crops Prod. 2020;158:112963.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwivedi A, Singh V, Anwar K, Pareek A, Jain M. Integrated transcriptome, proteome and metabolome analyses revealed secondary metabolites and auxiliary carbohydrate metabolism augmenting drought tolerance in rice. Plant Physiol Biochem. 2023;201:107849.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo X, Sun D, Wang S, Luo S, Fu Y, Niu L, Shi Q, Zhang Y. Integrating full-length transcriptomics and metabolomics reveals the regulatory mechanisms underlying yellow pigmentation in tree peony (Paeonia suffruticosa Andr.) flowers. Hortic Res. 2021; (001):008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWanli X, Xuli P, Zange J, Zili X. Genome-wide Identification and Expression Analysis of Chalcone Synthase Gene in Pepper. Genomics and Applied Biology; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSangkyu P, Min C, Jong L, Jae K, Sun-Hwa H, Sun-Hyung. Molecular and Biochemical Analysis of Two Rice Flavonoid 3'-Hydroxylase to Evaluate Their Roles in Flavonoid Biosynthesis in Rice Grain. Int J Mol Sci. 2016;17(9):1549.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong X, Chen W, Wang W, Zhang H, Liu X, Luo J. Comprehensive profiling and natural variation of flavonoids in rice. J Integr Plant Biol. 2014;56(9):876\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng Y, Li C, Li H, Lu S. molecules identification and characterization of flavonoid biosynthetic enzyme genes in salvia \u003cem\u003emiltiorrhiza\u003c/em\u003e (Lamiaceae). Molecules. 2018;23(6):1467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnna B, David R. The Roles of a Flavone-6-Hydroxylase and 7-O-Demethylation in the Flavone Biosynthetic Network of Sweet Basil. J Biol Chem. 2013;288(3):1795\u0026ndash;805.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDolferus R. To grow or not to grow: A stressful decision for plants - ScienceDirect. Plant Sci. 2014;229:247\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhanger MA, Morad-Talab N, Abd‐Allah EF, Ahmad P, Hajiboland R. Plant growth under drought stress. Plant growth under drought stress: Significance of mineral nutrients. Water Stress Crop Plants: Sustainable Approach. 2016;2:649\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JH, Cheon YM, Kim BG, Ahn JH. Analysis of Flavonoids and Characterization of the OsFNS Gene Involved in Flavone Biosynthesis in Rice. J Plant Biology. 2008;51:97\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee YJ, Kim JH, Kim BG, Lim Y, Ahn JH. Characterization of flavone synthase I from rice. BMB Rep. 2008;41(1):68\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng AX, Han XJ, Wu YF, Lou HX. The Function and Catalysis of 2-Oxoglutarate-Dependent Oxygenases Involved in Plant Flavonoid Biosynthesis. Int J Mol Sci. 2014;15(1):1080\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNan J, Andrea D, Erich G. Flavones: From Biosynthesis to Health Benefits. Plants 2016; 5(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartens S, Preu A, Matern U. Multifunctional flavonoid dioxygenases: flavonol and anthocyanin biosynthesis in Arabidopsis thaliana L. Phytochemistry. 2010;71(10):1040\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkel, Brenda SJ. Metabolic channeling in plants. Annu Rev Plant Biol. 2004;55:85\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShih CH, Chu H, Tang LK, Sakamoto W, Lo C. Functional characterization of key structural genes in rice flavonoid biosynthesis. Planta. 2008;228:1043\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Radwan MM, Tar\u0026aacute;wneh AH, Gao J, Wedge DE, Rosa LH, Cutler HG, Cutler SJ. Antifungal activity against plant pathogens of metabolites from the endophytic fungus Cladosporium cladosporioides. J Agric Food Chem. 2013;61(19):4551\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang D, Wang S, Lin L, Zhang J, Cui M, Wang S, Zhao X, Dong J, Long Y, Xing Z. Integrative Analysis of Metabolome and Transcriptome Reveals the Mechanism of Flavonoid Biosynthesis in. ACS Omega. 2022;7(23):19437\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Dixon RA. The 'ins' and 'outs' of flavonoid transport. Trends Plant Sci. 2010;15(2):72\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon RA. Engineering of plant natural product pathways. Curr Opin Plant Biol. 2005;8(3):329\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Chen B, Xiao J, Guo H. Different doses of UV-B radiation affect pigmented potatoes' growth and quality during the whole growth period. Front Plant Sci. 2023;14:1101172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDewey CN, Li B. Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323\u0026ndash;323.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove MI, Huber W, Anders SJ. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.\u003c/span\u003e\u003c/li\u003e\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":"transcriptome, metabolome, REL1, flavonoids, heat-tolerence","lastPublishedDoi":"10.21203/rs.3.rs-5406993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5406993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlants tend to produce special metabolites to resist biotic or abiotic invasions, in which flavonoid-mediated defense responses play an important role.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eIn our previous work, the \u003cem\u003erel1\u003c/em\u003e-D mutant was obtained by T-DNA insertion. Nearly all ZH11 died after 42 ℃ treatment, while nearly half of the mutants survived. By transcriptomic and metabolomic analysis of leaves, 1184 differentially expressed genes (DEGs) and 126 differentially accumulated metabolites (DAMs) were identified, most of these DEGs and DAMs were enriched in biosynthesis-related pathways such as the L-Phenylalanine pathway, flavonoid biosynthesis pathway and phenol pathway. Furthermore, a correlation network involved phenotypic traits was constructed based on the genes and metabolites.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePotential genes regulated by \u003cem\u003eREL1\u003c/em\u003e and flavonoid metabolites were identified. \u003cem\u003eREL1\u003c/em\u003e may affect the accumulation of flavonoid metabolites by regulating the expression of key genes in flavonoid biosynthesis pathway to influence the heat tolerance of rice.\u003c/p\u003e","manuscriptTitle":"Integrative Analysis of Flavonoid Pathways in Rice: Enhancing Heat Tolerance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 15:35:58","doi":"10.21203/rs.3.rs-5406993/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-14T08:10:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-09T13:34:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-09T13:32:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-11-07T05:40:28+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":"583b9bcd-d055-4d87-b761-92b5b1fa880f","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-08T16:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-5406993","link":"https://doi.org/10.1186/s12864-025-11977-0","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-09-01 15:57:15","publishedOnDateReadable":"September 1st, 2025"},"versionCreatedAt":"2024-11-27 15:35:58","video":"","vorDoi":"10.1186/s12864-025-11977-0","vorDoiUrl":"https://doi.org/10.1186/s12864-025-11977-0","workflowStages":[]},"version":"v1","identity":"rs-5406993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5406993","identity":"rs-5406993","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}