Integrated transcriptomics and metabolomics analyses provide insights into anthocyanin biosynthesis for leaf colour formation in Quercus mongolica

Integrated transcriptomics and metabolomics analyses provide insights into anthocyanin biosynthesis for leaf colour formation in Quercus mongolica | 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 Article Integrated transcriptomics and metabolomics analyses provide insights into anthocyanin biosynthesis for leaf colour formation in Quercus mongolica Yangchen Yuan, Jialin Liu, Xinman Li, Zipeng Zhao, Jiushuai Pang, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3845207/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Quercus mongolica is a tall tree with a broad, rounded crown and lush leaves. In autumn, the leaves turn red and have great ornamental value. However, the molecular mechanisms that cause the change in leaf colour are unknown. In this study, we identified 12 differentially expressed genes involved in anthocyanin synthesis by analysing the transcriptome of Q. mongolica leaves in six developmental stages (S1 − S6). We further analysed the dynamics of anthocyanin content in Q. mongolica leaves in four developmental stages (S1, S2, S5, and S6) using differential gene expression patterns. We detected a total of 48 anthocyanins and categorised these into seven major anthocyanin ligands. The most abundant anthocyanins in the red leaves of Q. mongolica were cyanidin-3,5-O-diglucoside, cyanidin-3-O-glucoside, cyanidin-3-O-sophoroside, and pelargonidin-3-O-glucoside. Correlation analysis of differentially expressed genes and anthocyanin content identified highly expressed QmANS as a key structural gene associated with anthocyanin biosynthesis in Q. mongolica . A transcription factor-structural gene correlation analysis showed that the 1bHLH, 3bZIP, 1MYB, 10NAC, and 2WRKY transcription factors played strong positive roles in regulating anthocyanin structural genes (|PCC| > 0.90), with the QmNAC transcription factor playing a major role in anthocyanin biosynthesis. Biological sciences/Biological techniques/Metabolomics Biological sciences/Biological techniques/Sequencing/Rna sequencing Biological sciences/Plant sciences/Plant molecular biology Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Gene regulation Biological sciences/Genetics/Genomics Biological sciences/Genetics/Plant genetics Biological sciences/Genetics/Sequencing Biological sciences/Plant sciences/Plant physiology Quercus mongolica Leaf colour Anthocyanin Transcriptomics Metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Anthocyanin is a water-soluble natural pigment that occurs extensively in plants [ 1 ]. Anthocyanidin glycosides are colour-presenting substances that determine the colour of plant leaves, flowers, and fruits, and are classed as flavonoid secondary metabolites [ 2 – 4 ]. More than 600 anthocyanins have been identified in nature [ 5 ], among which cyanidin, delphinidin, pelargonidin, peonidin, petunidin, and malvidin are the six most common anthocyanins in plants [ 6 ]. Delphinidin glycosides, malvidin glycosides, and petunidin glycosides are important colour-presenting substances in many blue–purple plant organs, whereas cyanidin glycosides and delphinidin glycosides are the main pigments in red plant organs. The degradation of chlorophyll occurs when plant leaves begin to age [ 7 ], whereas leaves turn red and yellow when anthocyanins and carotenoids, which are not easily degraded, accumulate in large quantities [ 8 ]. Anthocyanin biosynthesis is a branch of the flavonoid metabolic pathway. Their precursor phenylalanine is gradually converted into anthocyanins through phenylalanine ammonialyase ( PAL ), cinnamate 4-hydroxylase ( C4H ), 4-coumarate-CoA ligase ( 4CL ), chalcone synthase ( CHS ), chalcone isomerase ( CHI ), flavonoid 3’-hydroxylase ( F3'H ), flavonoid 3’, 5’- hydroxylase ( F3’5’H ), dihydroflavonol 4-reductase ( DFR ), anthocyanin synthase ( ANS ), and UDP glucose-flavonoid 3-O-glcosyl-transferase ( UFGT ) [ 9 , 10 ]. Subsequently, anthocyanins are accumulated and stored through anthocyanin glycosylation, glutathione S-transferase (GST) proteins, and multidrug and toxic compound extrusion (MATE) transporters [ 11 ]. Structural genes related to anthocyanin synthesis are synergistically regulated by the MYB-bHLH-WD40 (MBW) complex [ 12 ]. Transcription factors (TFs) such as NAC [ 13 ], WRKY [ 14 ], and bZIP [ 15 ] are also involved in the regulation of anthocyanins. Quercus mongolica (Fagaceae) is a deciduous broadleaf tree that is mainly distributed in Japan, Korea, the Russian Far East, the Korean Peninsula, and northern and northeastern China [ 16 ]. Quercus mongolica is an important timber [ 17 ], food [ 18 ], sericulture [ 19 ], medicinal [ 20 ], and landscape species [ 21 , 22 ] and is also used in the creation of windbreak forests, water conservation forests, and fire prevention forests. Although the yellow leaf metabolites of Q. mongolica have been studied previously [ 23 ], the molecular mechanism that causes red leaf colour changes remains unclear. In recent years, transcriptomics and metabolomics have been widely applied to explore relationships between genes and metabolites, and to identify structural genes and TFs that may be involved in secondary metabolic pathways [ 24 , 25 ]. For example, cyanidin-3,5-O-diglucoside is the major anthocyanin for the production of red leaves in Acer pseudosieboldianum , and PAL , ANS , DFR , and F3’H are structural genes involved in leaf colour production [ 26 ]. In a study on Phoebe bournei [ 27 ], cyanidin-3-O-glucoside was suggested to be a metabolite associated with red leaf colouration, and flavanone 3’-hydroxy-lase ( PbF3’H ) was significantly associated with cyanidin-3-O-glucoside. In this study, we investigated differences in anthocyanin biosynthesis during Q. mongolica leaf development. Candidate genes and regulators of anthocyanin biosynthesis were identified using transcriptomic analysis. Anthocyanin compounds at different leaf developmental stages were determined using multiple reaction monitoring (MRM). Then, unbiased network analysis was conducted to investigate the relationships between genes and anthocyanin accumulation. Our main objectives were to identify the potential key genes of the enzymes and TFs involved in anthocyanin biosynthesis pathways at different leaf developmental stages, and to explore the potential regulatory mechanisms of anthocyanin biosynthesis in leaves. The results of this study provided an in-depth understanding of anthocyanin biosynthesis in Q. mongolica leaves. 2. Materials and methods 2.1 Plant materials The study site was a natural Q. mongolica forest located in Caijiayu (39°32’6”N, 113°52’10”E; 1400 m a.s.l.) in Yixian County, Baoding, Hebei Province, China. Nine Q. mongolica trees exhibiting good and consistent growth were analysed. In 2022, leaf samples were collected at six developmental stages: May 10 (S1), August 4 (S2), August 31 (S3), September 21 (S4), October 9 (S5), and October 18 (S6) (Fig. 1 ). Functional leaves at the middle of new shoots were collected from east-, west-, south- and north-facing parts of the tree canopy. Leaf samples were immediately frozen in liquid nitrogen and refrigerated at − 80°C. The leaves of nine trees from each stage were mixed with three biological replicates for a total of 18 samples. We divided leaf growth and development into six developmental stages: the young leaf stage (S1), green leaf stage (S2–S4), colour change stage (S5), and red leaf stage (S6). 2.2 Measurement of leaf colour parameters Leaf colour parameters were analysed using a colourimeter (CR-400; Konica Minolta, Tokyo, Japan). Leaf colour was measured during six different sampling periods, and 30 leaves were randomly selected from each part of the tree canopy in each period for measurement. Ten leaves with similar growth were selected and their colour characteristics were measured at the tip and centre of the leaf as well as at the base of the petiole to obtain an average value. These measurements were repeated three times and the L*, a*, and b* values were recorded, where L* indicates brightness on a 0-100–point scale (black to white); a* represents colour on a green–red axis ranging from − 120 to 120, ranging from green to red at the positive and negative ends, respectively; and b* represents the blue–yellow axis, which also ranged from − 120 (blue) to 120 (yellow). 2.3 Determination of chlorophyll, carotenoid, and anthocyanin leaf contents Chlorophyll and carotenoid concentrations were determined by direct ethanol extraction[ 28 ]. Fresh leaves were washed with distilled water and the veins were removed. The fresh leaves were then cut into fine strips approximately 1 mm wide, weighed to the nearest 0.1 g and placed in a test tube to which 95% ethanol was added for a final volume of 10 mL. The test tube was sealed with plastic wrap and stored in the dark for 12–24 h until the leaf strips turned completely white. The solution was then aspirated into a cuvette. Chlorophyll content was calculated by measuring the optical density values at 665, 649 and 470 nm using a spectrophotometer, with 95% ethanol as a blank control. Anthocyanin content was determined using the method of Li et al. [ 29 ]. Fresh leaves were cut and weighed to the nearest 0.1 g in a triangular flask containing 10 mL of 1 mol L − 1 hydrochloric acid. This mixture was then placed in an oven at 32°C for 8 h, and then centrifuged. The supernatant was then collected to determine its optical density at 530 nm and calculate the anthocyanin content. 2.4 RNA extraction, transcriptome sequencing, and data analyses Eighteen independent RNA-seq libraries from a total of six groups of Q. mongolica samples (S1, S2, S3, S4, S5, and S6), each including three replicates, were constructed and sequenced. Total RNA was extracted from each sample according to the instructions for the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA). RNA quality was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked with RNase-free agarose gel electrophoresis. The cDNA fragments were purified using the QiaQuick PCR extraction kit. Eighteen cDNA libraries were prepared using the Illumina HiSeq4000 platform. Raw sequencing data were submitted to the National Center for Biotechnology Information (NCBI) Bioprojects database under project number PRJNA1048709. Raw reads from transcriptome sequencing were Fastp filtered for high-quality clean reads, and mapped reads for each sample were assembled using StringTie. FPKM values were calculated using the StringTie software, and then used to characterise differential gene expression between samples. To assess metabolic pathways and associated gene functions, we performed KEGG analysis of the DEGs (FDR < 0.05). For in-depth analysis of key regulatory genes regulating anthocyanin synthesis during leaf colour change in Q. mongolica , WGCNA was performed using the OmicShare tool ( http://www.omicsmart.com/ ). The correlation matrix was converted to a neighbour-joining matrix with power (soft threshold) = 14. A topological overlap matrix (TOM) was converted from the neighbour-joining matrix using a dissimilarity metric, a hierarchical clustering tree was constructed based on TOM similarity, and a dynamic tree-cutting algorithm (minModuleSize = 50, mergeCutHeight = 0.2) was used to filter similar modules in the hierarchical tree. Module signature genes were defined as the first principal component of a given module to represent the expression profile of the module genes in each sample. Pearson correlation of each gene under each module with the trait data was analysed to obtain the GS, where high GS values indicate significant genes for the phenotypic trait. The GS (correlation between gene and trait) and MM (correlation between gene expression and module) values of each module and each trait were analysed using Pearson correlation, where higher correlation coefficients indicated greater importance of the biological role played by the module in determining the trait. Genes with MM > 0.9 and GS > 0.6 were selected as central genes of the module, representing the expression trend of the whole module. To comprehensively analyse the regulatory genes that regulate anthocyanin synthesis during leaf colour changes in Q. mongolica , GSEA analysis was performed using the OmicShare tool ( http://www.omicsmart.com/ ). The expression information for all genes was used to rank the genes using Signal2Noise as a criterion. A specific gene set was analysed to determine whether its position in the ranking of all genes, and then the pathway in which the gene set was located was scored to derive an enrichment score (ES). A permutation test was performed based on the gene set, P values were calculated to evaluate significance, and finally the normalised ES (NES) value was corrected for multiple testing to obtain the FDR. Gene sets under pathways with |NES| > 1, P < 0.05, and FDR < 0.25 were considered significant. 2.5 Metabolomic analysis Leaf samples were freeze-dried and powdered in an MM 400 grinder (Retsh Technology, Haan, Germany). Then, a 50-mg powdered sample was extracted with 500 mL of 0.1% (v/v) hydrochloric methanol solution for 20 h at 4°C. The pigment extract sample was filtered through a HPLC PTFE syringe filter (0.22 mm). The anthocyanin composition was analysed using UPLC (ExionLC AD) equipped with a reverse-phase Acquity BEH C18 column (1.7 µm, 2.1 × 100 mm) (Waters Corp., Milford, MA, USA) and MS/MS (6500 QTRAP, Applied Biosystems, Waltham, MA, USA). UPLC analysis was performed under the following conditions: solvent system, ultrapure water (0.1% formic acid): methanol (0.1% formic acid); gradient program, 95:5 v/v at 0 min, 50:50 v/v at 6.0 min, 5:95 v/v at 2.0 min, and 95:5 v/v at 14.0 min; flow rate, 0.35 mL/min; temperature, 40°C; injection volume, 2 mL. The MS/MS data were analysed qualitatively based on the MetWare database (MetWare Biotechnology Co., Ltd., Wuhan, China). Anthocyanin concentrations were calculated using MRM. The MRM for each leaf sample was measured in triplicate. After unit variance scaling, a metabolite heatmap was generated by the complexheatmap v2.7.1.1009 package in R. Differentially accumulated metabolites were identified based on log 2 (FC) ≤ 0.5 or ≥ 2, and VIP > 1. 2.6 qRT-PCR analysis of key genes We conducted qRT-PCR analysis to verify the expression of genes in the transcriptome and coexpression network. The cDNA was synthesised using the M5 Sprint qPCR RT kit with gDNA remover (Mei5 Biotechnology Co., Beijing, China), and analysed using MagicSYBR Mixture (CWBIO, Beijing, China) in a StepOnePlus system (Thermo Fisher Scientific, Waltham, MA, USA) for qRT-PCR. The β-actin gene was used as an internal control [ 30 ], and primers were designed using Primer Premier 6.0. Relative expression levels were calculated using the 2 −ΔΔCT method [ 31 ]. 2.7 Statistical analyses All statistical analyses of leaf physiological data were performed in IBM SPSS Statistics 23 (IBM Corp., Armonk, NY, USA). Analysis of variance (ANOVA) and Duncan’s test were used to evaluate significant differences between samples ( P < 0.05). Data standardisation and principal component analysis (PCA) were performed in Origin 2019b, and OPLS-DA score plots, metabolite heatmaps, difference scatter plots, and weighted network plots were created using the omicshare online tool ( https://www.omicshare.come/tools/Home/Soft/get Soft). 3. Results 3.1 Changes in leaf colour parameters Leaf colour parameters changed during the development of Q. mongolica leaves, with L* showing a significant decrease followed by a significant increase and finally levelling off with leaf development. There were no significant differences in leaf colour parameters among stages S1, S5, and S6; however, they were significantly higher than those in stages S2, S3, and S4. This result indicates that leaf brightness was higher in young, colour transition, and senescence stages than during the green leaf stage. The leaf colour parameter a* displayed a significant increasing trend with continuous development of the leaf blade, and only the difference between stages S3 and S4 was not significant, indicating that the colour of the leaf blade changed from green to red. The leaf colour parameter b* initially displayed a significant decrease and then a significant increase with leaf blade development, and was significantly higher in the young leaf, colour change, and senescence stages than in the green leaf stage, and significantly higher in the green leaf stage than in stages S2, S3, and S4, which indicated that leaf blades turned yellow during the colour change and senescence stages (Fig. 2 a). 3.2 Changes in leaf pigment content Chlorophyll a and chlorophyll b contents in Q. mongolica leaves displayed an increasing and then decreasing trend over time. Carotenoid content displayed a trend of decreasing and then increasing over time. In the six stages, the values of total the carotenoids/chlorophyll ratio were 0.22, 0.13, 0.13, 0.14, 0.19, and 0.29, respectively, and the proportion of carotenoids first decreased and then increased continuously. Anthocyanin glycoside content in Q. mongolica leaves displayed a significant increasing trend over time. Overall, anthocyanin glycoside content in leaves displayed a significant increasing trend during development, chlorophyll content increased and then decreased, and carotenoid content displayed a decreasing and then a significant increasing trend (Fig. 2 b, c). 3.3 Anthocyanin metabolites in leaves The colour of Q. mongolica leaves gradually changed from green to deep red over the different developmental stages. For a deeper understanding of the differences in anthocyanin biosynthesis, we screened leaf samples from four stages (S1, S2, S5, and S6) for anthocyanin-targeted metabolome analysis based on the transcriptome results. Differences in anthocyanin metabolite contents in the leaves of stages S1, S2, S5, and S6 were evaluated using ultra-performance quadrupole–linear ion trap liquid chromatography–tandem mass spectrometry (UPLC/QTRAP-MS/MS). The results showed that total anthocyanin content increased gradually during leaf development. Total anthocyanin content was significantly lower in stages S1, S2, and S5 than in stage S6. A total of 56 metabolites were detected in Q. mongolica leaf samples (Fig. 3a), which were categorised into eight groups: cyanidins (12), delphinidins (10), malvidins (5), pelargonidins (5), peonidins (7), petunidins (7), proanthocyanidins (2), and flavonoids (8). The main components of anthocyanin metabolites during stage S6 were cyanidin-3-O-glucoside, pelargonidin-3-O-glucoside, cyanidin-3,5-O-diglucoside, and cyanidin-3-O-sophoroside. Overall, total anthocyanin metabolite content within Q. mongolica leaves displayed an increasing trend as the plant continued to develop. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed on the leaves (Fig. 3b). The OPLS-DA scoring plot showed clear separation between the developmental stages. The T score was 47.2% and the orthogonal T score was 13.9%. Segregation of anthocyanins between stages S1, S2, S5, and S6 indicated differences among leaf anthocyanin types and levels in the four developmental stages. A combined multivariate statistical analysis of variable importance in projection (VIP) and fold change (FC) values of the OPLS-DA results was conducted to screen for differential metabolites between the comparison groups, based on thresholds of VIP > 1 and FC ≥ 2 and FC ≤ 0.5. We compared groups S1, S2, and S5 with group S6. The S1 vs. S6 comparison had 40 differentially accumulated metabolites (DAMs) (29 upregulated, 11 downregulated), including 10 cyanidins, 5 delphinidins, 3 malvidins, 4 pelargonidins, 7 peonidins, 4 petunidins, 2 proanthocyanidins, and 5 flavonoids. The S2 vs. S6 comparison had 28 DAMs (25 upregulated, 3 downregulated), including 10 cyanidins, 4 delphinidins, 1 malvidin, 4 geraniolins, 3 pelargonidins, 1 petunidin, and 5 flavonoids. The S5 vs. S6 comparison had 22 DAMs (19 upregulated, 3 downregulated), including 10 cyanidin, 2 delphinidins, 1 malvidin, 3 pelargonidins, 2 peonidins, 1 petunidin, and 3 flavonoids (Supplementary Table 1). Differential metabolites common or unique to the three comparison groups (S1 vs. S6, S2 vs. S6, and S5 vs. S6) were visualised by a Venn analysis, with 46 differential metabolites shared among the three groups, and 15, 4, and 0 metabolites unique to the three groups, respectively (Fig. 4 a). A heatmap of the differential metabolites was created to visualise the accumulation of differential metabolites in the comparative groups (Fig. 4 b). The results suggested that upregulated expression of anthocyanin metabolites occurred in plant leaves during autumn. Figure 3. (a) Heatmap of all metabolites within a sample. (b) Orthogonal partial least-squares discriminant analysis scoring plot, where the horizontal and vertical axes indicate predicted orthogonal principal components, respectively, and horizontal and vertical distances indicate gaps between and within groups, respectively. Percentage indicates how well each component explains the dataset. Each point in the graph represents a sample, and samples from the same group are represented using the same colour. 3.4 Transcriptomic analysis during leaf development To determine the molecular mechanism of anthocyanin synthesis in leaves, we performed transcriptome analyses at six developmental stages to identify differential genes. A total of 115.33 GB of clean data was generated. The clean data of each library ranged from 5653766953 to 7489395393 bp, with an average score of 92.66–93.76% for Q30 bases. After filtering out uncertain reads, aptamer-related reads, and low-quality reads, we obtained clean data from the raw RNA sequencing (RNA-seq) data. These high-quality data provided assurance for further gene expression analyses. Based on thresholds of |log2(FC)| > 2 and false detection rate (FDR) < 0.05, we analysed differences among the 15 comparison groups. The results showed that among 15 permutations, there were large differences among the following comparison groups: S1 vs. S2, S1 vs. S4, S1 vs. S5, S1 vs. S6, S2 vs. S6, S3 vs. S6, S4 vs. S6, and S5 vs. S6. To investigate anthocyanin changes in Q. mongolica during different developmental stages, we analysed five comparison groups (S1 vs. S6, S2 vs. S6, S3 vs. S6, S4 vs. S6, and S5 vs. S6), and identified 14,688 differentially expressed genes (DEGs) (4,085 upregulated, 10,603 downregulated), 12,735 DEGs (3,594 upregulated, 9,141 downregulated), 12,618 DEGs (3,473 upregulated, 9,145 downregulated), 11,994 DEGs (1,609 upregulated, 4,151 downregulated), and 9,755 DEGs (2,942 upregulated, 6,813 downregulated), respectively (Fig. 5 ). Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis of DEGs at different stages revealed that metabolic pathways contributing to anthocyanin accumulation in Q. mongolica leaves, such as the biosynthesis of secondary metabolites, starch and sucrose metabolism, and photosynthesis, were significantly enriched in the different comparison groups (Supplementary Fig. 1). Metabolic pathways closely related to anthocyanin biosynthesis were annotated; these pathways included the phenylpropanoid (ko00940), flavonoid (ko00941), anthocyanidin (ko00942), isoflavonoid (ko00943), and flavonoid and flavonol (ko00944) biosynthesis pathways. To further understand the gene regulatory network and determine the genes affecting leaf colour changes in Q. mongolica , we performed weighted gene co-expression network analysis (WGCNA). Genes with fragments per kilobase of transcript per million mapped reads (FPKM) > 5 (15,220 genes) were used as source data to construct a scale-free co-expression network based on the β = 14 soft-threshold power value. Based on the WGCNA results, clusters of genes with high degrees of interconnectivity were defined as modules, where genes within the same module had high correlation coefficients. A total of 20 modules were identified using the dynamic tree-cutting method (mergeCutHeight = 0.2) (Fig. 6 a). The number of genes per module ranged from 31 to 2,674; the grey module was a collection of genes not assigned to other modules (31 genes). Correlation analysis of module eigenvalues and trait data showed that the blue, orange, dark grey, green, plum, midnight blue, yellow-green, and grey modules were highly significantly positively or negatively correlated with physiological indicators (Fig. 6 b). Correlation averages of genes within each module were obtained by calculating the correlation between gene and trait data and analysing the association of each module with the traits. The critical threshold for assigning gene significance (GS) was ≥ 0.6 (Fig. 6 c), and among the highly significantly correlated modules, blue (2,179 genes, r = 0.66, P < 0.01), green (2,422 genes, r = − 0.67, P < 0.01), plum (866 genes, r = − 0.73, P < 0.01), and yellow-green (103 genes, r = − 0.77, P < 0.01) were highly significantly associated with anthocyanins. The blue module (2,179 genes, r = 0.66, P < 0.01) was highly significantly positively correlated with a*, and the plum (866 genes, r = − 0.72, P < 0.01) and yellow-green modules (103 genes, r = − 0.75, P < 0.01) were highly significantly correlated with a*. In the blue module, genes were significantly enriched in the autophagy pathway. In the green module, genes were significantly enriched in the ribosomal pathway. In the plum module, genes were significantly enriched in the GPI-anchored protein biosynthesis pathway, RNA degradation, and spliceosome pathway. In the yellow-green module, genes were significantly enriched in the basic excision repair, DNA replication, and lipoic acid metabolism pathways. Thus, genes showing module membership (MM) > 0.9 and GS > 0.6 in the blue module were defined as central genes. We screened a total of 1,367 and 2,179 central genes related to anthocyanin synthesis and a*, respectively, and constructed a gene network diagram (Fig. 7 a, b). To comprehensively analyse the pathways affecting anthocyanin synthesis during leaf colour changes in Q. mongolica , we performed gene set enrichment analysis (GSEA) and found that the ko00943 pathway related to anthocyanin synthesis satisfied the condition of FDR (q) < 0.25 and P < 0.05, and was therefore significantly enriched (Fig. 8 ). 3.5 Differential expression of structural genes for anthocyanin biosynthesis Based on the KEGG, WGCNA, and GSEA analysis results, we identified 22 candidate genes involved in the anthocyanin biosynthesis pathway that showed high transcriptional activity in at least one leaf developmental stage, with high expression levels (FPKM > 10.0). These putative structural genes included QmPAL (4), Qm4CL (8), QmCHS (2), QmCHI (2), QmDFR (1), QmFLS (1), QmLAR (1), QmANS (1), QmUFGT (1), and QmCYP81E8 (1) (Supplementary Table 2). A comparison of the six stages revealed that all 22 candidate genes were DEGs. Each DEG had different expression patterns at the six developmental stages. Next, we examined DEGs that were expressed at extremely high levels (FPKM > 10) in at least one leaf developmental stage. A trend analysis of the expression of the 22 candidate genes is shown in Supplementary Fig. 2. 3.6 Correlation analysis of anthocyanin biosynthesis-related DEGs and DAMs To identify candidate regulatory genes involved in anthocyanin biosynthesis in Q. mongolica leaves, we performed correlation analysis of the 22 DEGs and 32 DAMs that were differentially expressed in stages S1, S2, S5, and S6 (Supplementary Fig. 3). We found that 12 differentially expressed anthocyanin structural genes were significantly correlated with 27 anthocyanins (|PCC| > 0.9, P < 0.01); their correlation coefficient weight network plots are shown in Fig. 9. These results suggest that these 12 structural genes may be influence anthocyanin biosynthesis in leaves. The relative expression of the four major anthocyanin metabolites pelargonidin-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside and cyanidin-3,5-O-diglucoside in stage S6 were highly significantly and positively correlated with QmCHS1 (gene-Qm025027), QmANS (gene-Qm031942), QmCYP81E8 (gene-Qm029525), and Qm4CL3 (gene-Qm020350) expression, respectively. These four genes were hypothesised to be the key genes affecting biosynthesis of the major anthocyanins in leaves. We mapped the structural genes and anthocyanin metabolite pathways that were differentially expressed in the anthocyanin biosynthesis pathway during leaf development (Fig. 10 ). The expression levels of one QmCHS gene (gene-Qm025027) and one QmANS (gene-Qm031942) gene increased with leaf development and were more than 16-fold higher at S6 than at S1. This finding was consistent with the significantly higher cyanidin-3-O-glucoside and pelargonidin-3-O-glucoside contents during S6 than during S1. 3.7 Correlation analysis of DEGs encoding TFs and differential anthocyanin metabolites To further identify candidate TFs involved in the regulation of anthocyanin biosynthesis in leaves, we analysed the MYB, bHLH, WRKY, NAC, bZIP, and HSF TFs, which were significantly differentially expressed at each development stage. We examined TFs with high expression levels in at least one leaf developmental stage (FPKM > 50) and screened 87 TFs, including 13 MYB, 13 bHLH, 19 WRKY, 22 NAC, 13 bZIP, and seven HSFTFs. We created a heatmap of the correlation between anthocyanin biosynthesis genes and 87 differential TFs, and selected a total of 12 anthocyanin biosynthesis genes in seven categories ( PAL , 4CL , CHI , CHS , CYP81E8 , FLS , and ANS ) that were previously screened as guide genes for correlation analysis (Supplementary Fig. 4). Among the differentially expressed TFs, 30 were highly significantly correlated with nine structural genes. Of these, 25 (5 MYB, 5 bHLH, 2 WRKY, 10 NAC, and 3 bZIP) TFs were highly significantly positively correlated with anthocyanin biosynthesis genes (|PCC| > 0.9, P 0.9, P < 0.01). Their weight network diagrams are shown in Fig. 11. In addition, correlation analysis of the differentially expressed TFs with QmCHS1 (gene-Qm025027), QmANS (gene-Qm031942), QmCYP81E8 (gene-Qm029525), and Qm4CL3 (gene-Qm020350) showed that 1 bHLH, 3 bZIP, 1 MYB, 10 NAC, and tw2o WRKY TFs played strong facilitating roles in regulating anthocyanin structural genes (Supplemental Table 3). The expression levels of QmNAC047 , QmNAC056 , QmNAC087 , and Qm NAP1 fluctuated slightly during stages S1–S4 and were extremely highly elevated in S5 and S6; their expression levels during S6 were 162-, 446-, 381-, and 1,812-fold higher than that during S1, respectively (Fig. 12), which suggests that QmNAC plays a major role in anthocyanin biosynthesis. Figure 11. Weighted network diagram of correlation coefficients between anthocyanin structural genes and transcription factors. 3.8 Validation of key DEGs involved in anthocyanin accumulation by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) To further confirm the reliability of the RNA-seq data, we performed qRT- PCR analysis of the four structural genes and five TFs involved in the anthocyanin biosynthesis pathway, and assessed differences in the transcript levels of a number of putative genes associated with anthocyanin accumulation. The results showed similar expression trends between the RNA-seq data and qRT-PCR results (Fig. 13 a). Linear regression analysis showed significant positive correlation between the RNA-seq and qRT-PCR data (Fig. 13 b), indicating the accuracy and reliability of the RNA-seq data. 4. Discussion The L*, a*, and b* leaf colour parameters express leaf colour in a precise, quantitative manner, with increasing L* values indicating a shift from dark to bright, a* values from negative to positive indicating a shift from green to red, and b* values from negative to positive indicating a shift from blue to yellow. In this study, the most variable leaf colour parameter was a*, which was negative in stages S1 − S4, and positive and increasing in stages S5 and S6. These results indicate that leaves changed from green to red during the period from S4 to S5. L* displayed an initially decreasing and then increasing trend. During S1, the leaf blades were young and bright, light green in colour. During S2–S4, leaf blades gradually turned dark green. During S5 and S6, leaf blades gradually turned red, with increased brightness. The b* values initially displayed a decreasing trend followed by an increasing trend, such that the leaf blades became more yellow during the later stages of development. As the leaves developed, their anthocyanin content increased continuously, becoming significantly higher during S5 and S6, indicating the rapid accumulation of anthocyanins in autumn, when the leaves turned red. Chlorophyll content increased and then decreased, indicating its continuous accumulation during leaf development, reaching a maximum in mature leaves. Chlorophyll declined in autumn, and anthocyanins accumulated rapidly, which caused the leaves to turn red. Carotenoid content initially decreased and then increased, reaching significantly higher levels during S6, indicating that carotenoids accumulated significantly in the late stage of leaf development. Anthocyanins are a class of water-soluble pigments in the vesicles of plant epidermal cells that impart a wide range of colours to plant organs and tissues. The accumulation of anthocyanins in plant organs is a key trait affecting plant quality and ornamental value [ 32 , 33 ]. In this study, we integrated metabolomic and transcriptomic analyses to identify the anthocyanin components and genes involved in anthocyanin biosynthesis during leaf development. In recent years, the anthocyanin contents and fractions of different plant leaves have been reported. For example, the major anthocyanins in the zikui ( Camellia sinensis ) tea tree are petunidin-3-O-glucoside, cyanidin-3-O-galactoside, and cyanidin-3-O-glucoside [ 34 ]. Studies conducted on Quercus aliena leaves revealed that the accumulation of anthocyanins such as cyanidin-3-O-glucoside and cyanidin-3-O-sambubiglycoside had significant effects on leaf colouration [ 35 ]. In acer triflorum , cyanidin-3-O-arabinoside is closely related to red leaf colour [ 36 ]. The major anthocyanin in the leaves of Phoebe bournei is cyanidin-3-O-glucoside [ 27 ]. Based on anthocyanin-targeted metabolome analysis, we further identified 48 anthocyanins differential metabolites and found that pelargonidin-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside, and cyanidin-3,5-O-diglucoside were the major anthocyanins in red leaves of Q. mongolica . By analysing the anthocyanin fractions of developing leaves, we found that the contents of most delphinidin, malvidin, peonidin, and petunidin derivatives were extremely low or decreased with the prolongation of each developmental stage. This result suggests that the delphinidin, malvidin, peonidin, and petunidin fractions were not the major factors driving leaf colour changes. Additionally, pelargonidin-3-O-glucoside, cyanidin-3,5-O-diglucoside, cyanidin-3-O-glucoside, and cyanidin-3-O-sophoroside contents tended to increase with developmental stage, and cyanidin-3-O-glucoside displayed a highly significant increasing trend, suggesting that high levels of blue pigment components in these derivatives may be essential for red leaf colour in Q. mongolica ; this finding is consistent with previous studies on ornamental plants. The regulatory mechanisms of colour formation are closely related to the expression of anthocyanin structural genes and TFs [ 37 , 38 ]. Anthocyanin biosynthesis is a branch of the flavonoid synthesis pathway starting from phenylalanine, and involving a variety of enzymes encoded by early biosynthesis genes ( PAL , 4CL , C4H , CHS , CHI , F3H , F3’H , and F3’5’H ) and anthocyanin biosynthesis genes ( DFR , ANS , and UFGT ) [ 39 ]. Previous studies have shown that several key genes and enzymes are required for anthocyanin accumulation and leaf colour formation in many ornamental plants, such as Quercus dentata [ 40 ] and cassava ( Manihot esculenta ) [ 41 ]. We identified 22 DEGs associated with anthocyanin biosynthesis in Q. mongolica leaves, including 16 genes for early biosynthesis, one for proanthocyanidin synthesis, one for flavonol synthesis, one for isoflavone synthesis, and three for anthocyanin biosynthesis. The expression levels of QmCYP81E8 , Qm4CL , QmCHS , QmANS , QmCHI , QmFLS , QmUFGT , and QmDFR were significantly positively correlated with the anthocyanin content in this study. Among these, QmANS , QmCHS1 , QmCYP81E8 , and Qm4CL3 were strongly positively correlated with the four major colourants. In this study, QmANS and QmCHS1 gene expression levels were significantly upregulated during S5 and S6. In the anthocyanidin synthesis pathway, anthocyanidin synthase ( ANS ) catalyses the formation of colourless anthocyanidin glycosides to construct colourful anthocyanidin glycosides, and this intermediate product is further coupled with 3-O-glucosyltransferase (3GT) and transported to vesicles to form colourful 3-O-glycosidised anthocyanosides [ 42 ], which play important roles in the formation of plant colour. In the peony Paeonia suffruticosa , PaANS is expressed at higher levels in purplish-red leaves than in yellow-green leaves [ 43 ]. Other studies have shown that the overexpression of SmANS increases anthocyanin content in Salvia officinalis , whereas low SmANS expression in the flowers of Salvia officinalis resulted in white colouration [ 44 , 45 ]. Upregulated CsANS expression in the zikui tea tree ( Camellia sinensis cv. ‘Zikui’) may promote anthocyanin accumulation [ 34 ]. In the flowers of a Cymbidium hybrid, the expression of ChANS and ChDFR was found to be closely related to anthocyanin accumulation patterns [ 38 ]. RsPAL , Rs4CL , RsCHS , RsCHI , and RsDFR enzymes are more highly expressed in the red variant of radish ( Raphanus sativus ) than in the white variant, and RsCHS plays an important role in radish root colouration [ 46 ]. It has been suggested that QaCHS1 , QaCHI , and QaANS1 may be the key regulators of Quercus aliena anthocyanin synthesis [ 35 ]. Studies on the safflower Carthamus tinctorius have shown that transcriptional regulation of CtCYP81E8 is associated with flavonoid accumulation [ 47 ]. Because QmANS is an anthocyanin synthesis gene, it is hypothesised to be a key determinant of Q. mongolica leaf reddening. In addition to structural genes, TFs play important roles in the regulation of anthocyanin biosynthesis. We analysed MYB, bHLH, WRKY, bZIPs, NAC, and HSF. Previous studies have shown that MYB-bHLH-WD40 is a major class of TFs regulating structural genes of the anthocyanin pathway, as has been reported in Arabidopsis [ 48 ], grape [ 49 ], persimmon [ 50 ] and strawberry [ 51 ]. It has also been reported that the R2R3-MYB gene controls the expression of the key genes CHS, CHI, and F3’H in anthocyanin biosynthesis. In this study, one bHLH, three bZIP, one MYB, 10 NAC, and two WRKY TFs were strongly positively correlated with the expression of structural genes, and were identified from the DEG data as candidate TFs. We then performed a phylogenetic analysis of the predicted protein sequences of five candidate MYB transcription factors as well as MYB gene family proteins from Arabidopsis (Supplementary Fig. 5). The phylogenetic tree showed that the TF QmSRM1 belongs to the same branch as AtMYB1 , AtMYB43 , and AtMYB78 in Arabidopsis. The R2R3-MYB TF RcMYB1 plays a central role in the biosynthesis of rose anthocyanins [ 52 ]. Therefore, we hypothesise that QmSRM1 may be involved in the synthesis and accumulation of anthocyanin. NAC TFs also play a role in regulating anthocyanin accumulation. We performed a phylogenetic analysis of the predicted protein sequences of seven candidate NAC TFs and NAC gene family proteins from Arabidopsis (Supplementary Fig. 6). The phylogenetic tree showed that QmNAC087 belongs to the same branch as AtNAC087 in Arabidopsis. In Arabidopsis, AtNAC087 overexpression induces anthocyanin accumulation, decreases chlorophyll content, and enhances the expression of leaf senescence-related genes. The expression of AfNAC083 is positively correlated with the anthocyanin content in Acer fabri [ 53 ]. IbNAC056a/b in sweet potato promotes anthocyanin accumulation [ 54 ]. IbNAC056 , QmNAC056 , and QmNAC047 were clustered in the same branch as AtNAC056 , and it has been hypothesised that QmNAC056 and QmNAC047 promote anthocyanin accumulation. In this study, QmNAC073 , QmNAP1 , and QmJA2L were positively correlated with anthocyanin accumulation, and were hypothesised to be involved in anthocyanin synthesis regulation. Overexpression of DcNAP1 promotes leaf senescence in Dianthus caryophyllus [ 55 ] and Panicum virgatum [ 56 ]. We hypothesised that this process is related to anthocyanin accumulation, which requires further investigation in future studies. Other TFs also play roles in regulating anthocyanin accumulation. This study showed that QmWRKY72A , QmWRKY75 , QmILR3 (bHLH), QmbZIP53 , and QmCPRF2 (bZIP) were significantly positively correlated with structural genes. MdWRKY72 [ 14 ] and MdWRKY75 [ 57 ] have been shown to regulate anthocyanin synthesis in apple fruit. According to protein interactions, ILR3 is an important bHLH interacting with the MYB, RGA, CRY1, and bZIP proteins, which regulate anthocyanin biosynthesis during the sesame life cycle [ 58 ]. We hypothesised that QmWRKY72A , QmWRKY75 , and QmILR3 (bHLH) regulate anthocyanin accumulation in Q. mongolica leaves. Chrysanthemum flowers have been shown to become lighter in colour as anthocyanin levels decrease and bZIP53 expression is upregulated [ 59 ], which is inconsistent with the present findings. The phosphorylation state of CPRF2 (bZIP), which is a phosphorylated protein, increases rapidly in response to light [ 60 ]. CPRF2 (bZIP) is negatively correlated with anthocyanin accumulation in Dendrobium chrysotoxum [ 61 ], which is inconsistent with the results of the present study; therefore, we hypothesise that the leaves of D. chrysotoxum and Q. mongolica have different colouration mechanisms. Our TF expression analyses showed that the expression of NAC TFs was significantly higher than that of other TFs during phases S5 and S6. The expression levels of QmNAC047 , QmNAC056 , QmNAC087 , and QmNAP1 fluctuated slightly during S1–S4 and were extremely elevated during S5 and S6. Expression levels at S6 were 162, 446, 381, and 1812 times higher than those at S1, respectively. Therefore, we hypothesise that QmNAC047 , QmNAC056 , QmNAC087 , and QmNAP1 play key regulatory roles in anthocyanin synthesis. 5 Conclusion In this study, we present detailed potential pathways for anthocyanin biosynthesis. Our findings demonstrate that pelargonidinl-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside, and cyanidin-3,5-O-diglucoside are the main colour-presenting substances in red leaves of Q. mongolica . In the anthocyanin biosynthesis pathway, a total of 12 structural genes and 17 TFs were found to be involved in the regulation of anthocyanin biosynthesis, and QmANS , QmNAC047 , QmNAC056 , QmNAC087 , and QmNAP1 were found to be the likely key determinants of anthocyanin biosynthesis. Further studies of the precise functional expression mechanisms of the transgenes are needed to validate their role in the regulation of anthocyanin biosynthesis. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/ty7v5N Declarations Data Availability Statement All relevant data are within the paper and its Supporting information files. The datasets generated or analysed during the current study are available in the NCBI repository, PRJNA1048709. A cknowledgments We acknowledge all the members of the research group for their helpful comments and inspiration. Competing I nterests The authors have declared that no competing interests exist. 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Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACAzDJBmNUSMjxk6jljIWxZANJWhjbKhI3ENJiLpH87OGXMjt7MOPrPAnGDQzMDx/dwKPFckaaubHMuWRmMEN2mwSzOQObsXEOPofdSDCTlmxjZoMwtkmwWTbwsEnj15L+DailngfCmCPBY3CAoJYcM8mPbYclIIwGCQnCWs68KZNmOHfcAMI4JmEg2UzIL8fTt0n+KKu2hzBq6ur72ZsfPsanBQSYeVAYzASUgwDjD3TGKBgFo2AUjAJkAADz0Eghb2OpogAAAABJRU5ErkJggg==","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Dazhuang","middleName":"","lastName":"Huang","suffix":""},{"id":267590823,"identity":"835721d6-763c-44f5-8684-dee31a325189","order_by":14,"name":"Minsheng Yang","email":"","orcid":"","institution":"Hebei Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Minsheng","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-01-08 10:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3845207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3845207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49805500,"identity":"ccd3e34f-4305-455e-b26d-6c8ff32b5ecc","added_by":"auto","created_at":"2024-01-18 10:14:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167894,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotypes of \u003cem\u003eQ. mongolica\u003c/em\u003e in six developmental stages: young leaf stage (S1), green leaf stage (S2–S4), colour change stage (S5), and red leaf stage (S6).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/d51222e6887de5f8ba96dca4.png"},{"id":49805142,"identity":"6a4d318d-1331-4e6d-b106-fa32281ae768","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":106565,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Changes in the leaf colour parameters. (b) Anthocyanin content and variation.（c） Chlorophyll and carotenoid contentand variation. Different lowercase letters indicate significant differences between different groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).The last number is the standard deviation.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/02b2ee9cfa1f576b461317f2.png"},{"id":49805144,"identity":"dbd9ca26-1122-4d23-8adb-09c2b4743c0a","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268525,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Heatmap of all metabolites within a sample. (b) Orthogonal partial least-squares discriminant analysis scoring plot, where the horizontal and vertical axes indicate predicted orthogonal principal components, respectively, and horizontal and vertical distances indicate gaps between and within groups, respectively. Percentage indicates how well each component explains the dataset. Each point in the graph represents a sample, and samples from the same group are represented using the same colour.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/9e7fa383290828c15f8c38a3.png"},{"id":49806063,"identity":"ec065338-8c46-496e-97ad-2a3166ef1872","added_by":"auto","created_at":"2024-01-18 10:22:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":533489,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Venn diagram and (b) heatmap of differential metabolites.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/d4cc213edd37214b72ea2250.png"},{"id":49805153,"identity":"89651a47-ddc4-4e6f-9385-736f5dee54da","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1004315,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano maps of differential metabolites.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/4a5173fe8a1750afd8cd0657.png"},{"id":49805152,"identity":"08180bd6-db63-44cc-9a5b-09754a785adf","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":604598,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) co-expression network and module–trait correlation analysis results. (a) Hierarchical clustering tree of co-expression modules identified using the dynamic tree-cutting method. (b) Correlation of physiological indicators with WGCNA modules (numbers in cells are correlation coefficients; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). (c) Correlation of anthocyanins and a* with WGCNA modules.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/5da5e34ecaf19497f056332c.png"},{"id":49805155,"identity":"157d275f-154d-40ab-b478-62b875521ab3","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":747023,"visible":true,"origin":"","legend":"\u003cp\u003eGene regulatory network maps of the (a) top 10 genes in terms of module membership values and (b) top 10 transcription factor genes in terms connectivity.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/6a8ef37002e751287e1c4ba1.png"},{"id":49805502,"identity":"4fa8b2c6-7690-4a81-9fe4-561c1f1b6153","added_by":"auto","created_at":"2024-01-18 10:14:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":396047,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment score diagrams, indicating significant enrichment of the ko00943 pathway.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/8f552fd6758675a2432efdcb.png"},{"id":49805150,"identity":"c93dd962-65e9-4275-84b7-e32e295cd325","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":382964,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted network diagram of correlation coefficients between anthocyanin structural genes and anthocyanin metabolites.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/de6826a4758504e74215cc1b.png"},{"id":49805145,"identity":"3c8f881c-4d39-433d-93ae-884c1e7cf51d","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":59036,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed structural genes and anthocyanin metabolites in the anthocyanin biosynthetic pathway during leaf development. \u003cem\u003ePAL\u003c/em\u003e, phenylalanine ammonia-lyase; \u003cem\u003eC4H\u003c/em\u003e, cinnamic acid 4-hydroxylase; \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\u003eDFR\u003c/em\u003e, dihydroflavonol 4-reductase; \u003cem\u003eANS\u003c/em\u003e, anthocyanidin synthase; \u003cem\u003eUFGT\u003c/em\u003e, \u003cem\u003eUDP\u003c/em\u003e glucose-flavonoid 3-O-glcosyl-transferase.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/83ee3588c1c44a96fd8cc385.png"},{"id":49805504,"identity":"8c417e88-9bcd-43b0-947a-57dbabd7dc85","added_by":"auto","created_at":"2024-01-18 10:14:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":277949,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted network diagram of correlation coefficients between anthocyanin structural genes and transcription factors.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/0087898fe22f2e0139be21b0.png"},{"id":49805501,"identity":"ab008261-53f0-4dd0-8352-cefb077a48a4","added_by":"auto","created_at":"2024-01-18 10:14:08","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":15104,"visible":true,"origin":"","legend":"\u003cp\u003eFragments per kilobase of transcript per million mapped reads (FPKM) values for four NAC transcription factors at different times.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/750489c55cfe40146649ea3d.png"},{"id":49805147,"identity":"c3359069-0b7e-40f8-8834-e02b48c0bdf6","added_by":"auto","created_at":"2024-01-18 10:06:08","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":133487,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of \u003cem\u003eQ. mongolica\u003c/em\u003e(a) RNA sequencing (RNA-seq) data and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis results through (b) comparison of the log2 gene expression ratios between the RNA-seq and qRT-PCR results for structural genes and transcription factors, respectively.\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/3870da7a3d67049ff9146f75.png"},{"id":67180815,"identity":"30763364-0544-4c20-a1a6-a6470839719e","added_by":"auto","created_at":"2024-10-22 06:10:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5310196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/05ad4ed4-1b34-4fed-93a2-b5ea7c25e59a.pdf"},{"id":49805156,"identity":"7af5ed37-fab1-4d27-baeb-a5079c3a8a65","added_by":"auto","created_at":"2024-01-18 10:06:09","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14592594,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-3845207/v1/8f1c267f22aedaa225f9cfd3.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated transcriptomics and metabolomics analyses provide insights into anthocyanin biosynthesis for leaf colour formation in Quercus mongolica","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAnthocyanin is a water-soluble natural pigment that occurs extensively in plants [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Anthocyanidin glycosides are colour-presenting substances that determine the colour of plant leaves, flowers, and fruits, and are classed as flavonoid secondary metabolites [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. More than 600 anthocyanins have been identified in nature [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], among which cyanidin, delphinidin, pelargonidin, peonidin, petunidin, and malvidin are the six most common anthocyanins in plants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Delphinidin glycosides, malvidin glycosides, and petunidin glycosides are important colour-presenting substances in many blue\u0026ndash;purple plant organs, whereas cyanidin glycosides and delphinidin glycosides are the main pigments in red plant organs. The degradation of chlorophyll occurs when plant leaves begin to age [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], whereas leaves turn red and yellow when anthocyanins and carotenoids, which are not easily degraded, accumulate in large quantities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Anthocyanin biosynthesis is a branch of the flavonoid metabolic pathway. Their precursor phenylalanine is gradually converted into anthocyanins through phenylalanine ammonialyase (\u003cem\u003ePAL\u003c/em\u003e), cinnamate 4-hydroxylase (\u003cem\u003eC4H\u003c/em\u003e), 4-coumarate-CoA ligase (\u003cem\u003e4CL\u003c/em\u003e), chalcone synthase (\u003cem\u003eCHS\u003c/em\u003e), chalcone isomerase (\u003cem\u003eCHI\u003c/em\u003e), flavonoid 3\u0026rsquo;-hydroxylase (\u003cem\u003eF3'H\u003c/em\u003e), flavonoid 3\u0026rsquo;, 5\u0026rsquo;- hydroxylase (\u003cem\u003eF3\u0026rsquo;5\u0026rsquo;H\u003c/em\u003e), dihydroflavonol 4-reductase (\u003cem\u003eDFR\u003c/em\u003e), anthocyanin synthase (\u003cem\u003eANS\u003c/em\u003e), and UDP glucose-flavonoid 3-O-glcosyl-transferase (\u003cem\u003eUFGT\u003c/em\u003e) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Subsequently, anthocyanins are accumulated and stored through anthocyanin glycosylation, glutathione S-transferase (GST) proteins, and multidrug and toxic compound extrusion (MATE) transporters [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Structural genes related to anthocyanin synthesis are synergistically regulated by the MYB-bHLH-WD40 (MBW) complex [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Transcription factors (TFs) such as NAC [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], WRKY [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and bZIP [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] are also involved in the regulation of anthocyanins.\u003c/p\u003e \u003cp\u003e \u003cem\u003eQuercus mongolica\u003c/em\u003e (Fagaceae) is a deciduous broadleaf tree that is mainly distributed in Japan, Korea, the Russian Far East, the Korean Peninsula, and northern and northeastern China [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. \u003cem\u003eQuercus mongolica\u003c/em\u003e is an important timber [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], food [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], sericulture [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], medicinal [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and landscape species [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and is also used in the creation of windbreak forests, water conservation forests, and fire prevention forests. Although the yellow leaf metabolites of \u003cem\u003eQ. mongolica\u003c/em\u003e have been studied previously [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the molecular mechanism that causes red leaf colour changes remains unclear.\u003c/p\u003e \u003cp\u003eIn recent years, transcriptomics and metabolomics have been widely applied to explore relationships between genes and metabolites, and to identify structural genes and TFs that may be involved in secondary metabolic pathways [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For example, cyanidin-3,5-O-diglucoside is the major anthocyanin for the production of red leaves in \u003cem\u003eAcer pseudosieboldianum\u003c/em\u003e, and \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eANS\u003c/em\u003e, \u003cem\u003eDFR\u003c/em\u003e, and \u003cem\u003eF3\u0026rsquo;H\u003c/em\u003e are structural genes involved in leaf colour production [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a study on \u003cem\u003ePhoebe bournei\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], cyanidin-3-O-glucoside was suggested to be a metabolite associated with red leaf colouration, and flavanone 3\u0026rsquo;-hydroxy-lase (\u003cem\u003ePbF3\u0026rsquo;H\u003c/em\u003e) was significantly associated with cyanidin-3-O-glucoside.\u003c/p\u003e \u003cp\u003eIn this study, we investigated differences in anthocyanin biosynthesis during \u003cem\u003eQ. mongolica\u003c/em\u003e leaf development. Candidate genes and regulators of anthocyanin biosynthesis were identified using transcriptomic analysis. Anthocyanin compounds at different leaf developmental stages were determined using multiple reaction monitoring (MRM). Then, unbiased network analysis was conducted to investigate the relationships between genes and anthocyanin accumulation. Our main objectives were to identify the potential key genes of the enzymes and TFs involved in anthocyanin biosynthesis pathways at different leaf developmental stages, and to explore the potential regulatory mechanisms of anthocyanin biosynthesis in leaves. The results of this study provided an in-depth understanding of anthocyanin biosynthesis in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials\u003c/h2\u003e \u003cp\u003eThe study site was a natural \u003cem\u003eQ. mongolica\u003c/em\u003e forest located in Caijiayu (39\u0026deg;32\u0026rsquo;6\u0026rdquo;N, 113\u0026deg;52\u0026rsquo;10\u0026rdquo;E; 1400 m a.s.l.) in Yixian County, Baoding, Hebei Province, China. Nine \u003cem\u003eQ. mongolica\u003c/em\u003e trees exhibiting good and consistent growth were analysed. In 2022, leaf samples were collected at six developmental stages: May 10 (S1), August 4 (S2), August 31 (S3), September 21 (S4), October 9 (S5), and October 18 (S6) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Functional leaves at the middle of new shoots were collected from east-, west-, south- and north-facing parts of the tree canopy. Leaf samples were immediately frozen in liquid nitrogen and refrigerated at \u0026minus;\u0026thinsp;80\u0026deg;C. The leaves of nine trees from each stage were mixed with three biological replicates for a total of 18 samples. We divided leaf growth and development into six developmental stages: the young leaf stage (S1), green leaf stage (S2\u0026ndash;S4), colour change stage (S5), and red leaf stage (S6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of leaf colour parameters\u003c/h2\u003e \u003cp\u003eLeaf colour parameters were analysed using a colourimeter (CR-400; Konica Minolta, Tokyo, Japan). Leaf colour was measured during six different sampling periods, and 30 leaves were randomly selected from each part of the tree canopy in each period for measurement. Ten leaves with similar growth were selected and their colour characteristics were measured at the tip and centre of the leaf as well as at the base of the petiole to obtain an average value. These measurements were repeated three times and the L*, a*, and b* values were recorded, where L* indicates brightness on a 0-100\u0026ndash;point scale (black to white); a* represents colour on a green\u0026ndash;red axis ranging from \u0026minus;\u0026thinsp;120 to 120, ranging from green to red at the positive and negative ends, respectively; and b* represents the blue\u0026ndash;yellow axis, which also ranged from \u0026minus;\u0026thinsp;120 (blue) to 120 (yellow).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination of chlorophyll, carotenoid, and anthocyanin leaf contents\u003c/h2\u003e \u003cp\u003eChlorophyll and carotenoid concentrations were determined by direct ethanol extraction[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Fresh leaves were washed with distilled water and the veins were removed. The fresh leaves were then cut into fine strips approximately 1 mm wide, weighed to the nearest 0.1 g and placed in a test tube to which 95% ethanol was added for a final volume of 10 mL. The test tube was sealed with plastic wrap and stored in the dark for 12\u0026ndash;24 h until the leaf strips turned completely white. The solution was then aspirated into a cuvette. Chlorophyll content was calculated by measuring the optical density values at 665, 649 and 470 nm using a spectrophotometer, with 95% ethanol as a blank control.\u003c/p\u003e \u003cp\u003eAnthocyanin content was determined using the method of Li et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Fresh leaves were cut and weighed to the nearest 0.1 g in a triangular flask containing 10 mL of 1 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e hydrochloric acid. This mixture was then placed in an oven at 32\u0026deg;C for 8 h, and then centrifuged. The supernatant was then collected to determine its optical density at 530 nm and calculate the anthocyanin content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 RNA extraction, transcriptome sequencing, and data analyses\u003c/h2\u003e \u003cp\u003eEighteen independent RNA-seq libraries from a total of six groups of \u003cem\u003eQ. mongolica\u003c/em\u003e samples (S1, S2, S3, S4, S5, and S6), each including three replicates, were constructed and sequenced. Total RNA was extracted from each sample according to the instructions for the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA). RNA quality was evaluated using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and checked with RNase-free agarose gel electrophoresis. The cDNA fragments were purified using the QiaQuick PCR extraction kit. Eighteen cDNA libraries were prepared using the Illumina HiSeq4000 platform. Raw sequencing data were submitted to the National Center for Biotechnology Information (NCBI) Bioprojects database under project number PRJNA1048709. Raw reads from transcriptome sequencing were Fastp filtered for high-quality clean reads, and mapped reads for each sample were assembled using StringTie. FPKM values were calculated using the StringTie software, and then used to characterise differential gene expression between samples. To assess metabolic pathways and associated gene functions, we performed KEGG analysis of the DEGs (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor in-depth analysis of key regulatory genes regulating anthocyanin synthesis during leaf colour change in \u003cem\u003eQ. mongolica\u003c/em\u003e, WGCNA was performed using the OmicShare tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omicsmart.com/\u003c/span\u003e\u003cspan address=\"http://www.omicsmart.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The correlation matrix was converted to a neighbour-joining matrix with power (soft threshold)\u0026thinsp;=\u0026thinsp;14. A topological overlap matrix (TOM) was converted from the neighbour-joining matrix using a dissimilarity metric, a hierarchical clustering tree was constructed based on TOM similarity, and a dynamic tree-cutting algorithm (minModuleSize\u0026thinsp;=\u0026thinsp;50, mergeCutHeight\u0026thinsp;=\u0026thinsp;0.2) was used to filter similar modules in the hierarchical tree. Module signature genes were defined as the first principal component of a given module to represent the expression profile of the module genes in each sample. Pearson correlation of each gene under each module with the trait data was analysed to obtain the GS, where high GS values indicate significant genes for the phenotypic trait. The GS (correlation between gene and trait) and MM (correlation between gene expression and module) values of each module and each trait were analysed using Pearson correlation, where higher correlation coefficients indicated greater importance of the biological role played by the module in determining the trait. Genes with MM\u0026thinsp;\u0026gt;\u0026thinsp;0.9 and GS\u0026thinsp;\u0026gt;\u0026thinsp;0.6 were selected as central genes of the module, representing the expression trend of the whole module.\u003c/p\u003e \u003cp\u003eTo comprehensively analyse the regulatory genes that regulate anthocyanin synthesis during leaf colour changes in \u003cem\u003eQ. mongolica\u003c/em\u003e, GSEA analysis was performed using the OmicShare tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omicsmart.com/\u003c/span\u003e\u003cspan address=\"http://www.omicsmart.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expression information for all genes was used to rank the genes using Signal2Noise as a criterion. A specific gene set was analysed to determine whether its position in the ranking of all genes, and then the pathway in which the gene set was located was scored to derive an enrichment score (ES). A permutation test was performed based on the gene set, \u003cem\u003eP\u003c/em\u003e values were calculated to evaluate significance, and finally the normalised ES (NES) value was corrected for multiple testing to obtain the FDR. Gene sets under pathways with |NES| \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metabolomic analysis\u003c/h2\u003e \u003cp\u003eLeaf samples were freeze-dried and powdered in an MM 400 grinder (Retsh Technology, Haan, Germany). Then, a 50-mg powdered sample was extracted with 500 mL of 0.1% (v/v) hydrochloric methanol solution for 20 h at 4\u0026deg;C. The pigment extract sample was filtered through a HPLC PTFE syringe filter (0.22 mm). The anthocyanin composition was analysed using UPLC (ExionLC AD) equipped with a reverse-phase Acquity BEH C18 column (1.7 \u0026micro;m, 2.1 \u0026times; 100 mm) (Waters Corp., Milford, MA, USA) and MS/MS (6500 QTRAP, Applied Biosystems, Waltham, MA, USA). UPLC analysis was performed under the following conditions: solvent system, ultrapure water (0.1% formic acid): methanol (0.1% formic acid); gradient program, 95:5 v/v at 0 min, 50:50 v/v at 6.0 min, 5:95 v/v at 2.0 min, and 95:5 v/v at 14.0 min; flow rate, 0.35 mL/min; temperature, 40\u0026deg;C; injection volume, 2 mL. The MS/MS data were analysed qualitatively based on the MetWare database (MetWare Biotechnology Co., Ltd., Wuhan, China). Anthocyanin concentrations were calculated using MRM. The MRM for each leaf sample was measured in triplicate. After unit variance scaling, a metabolite heatmap was generated by the \u003cem\u003ecomplexheatmap\u003c/em\u003e v2.7.1.1009 package in R. Differentially accumulated metabolites were identified based on log\u003csub\u003e2\u003c/sub\u003e(FC)\u0026thinsp;\u0026le;\u0026thinsp;0.5 or \u0026ge;\u0026thinsp;2, and VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 qRT-PCR analysis of key genes\u003c/h2\u003e \u003cp\u003eWe conducted qRT-PCR analysis to verify the expression of genes in the transcriptome and coexpression network. The cDNA was synthesised using the M5 Sprint qPCR RT kit with gDNA remover (Mei5 Biotechnology Co., Beijing, China), and analysed using MagicSYBR Mixture (CWBIO, Beijing, China) in a StepOnePlus system (Thermo Fisher Scientific, Waltham, MA, USA) for qRT-PCR. The β-actin gene was used as an internal control [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and primers were designed using Primer Premier 6.0. Relative expression levels were calculated using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses of leaf physiological data were performed in IBM SPSS Statistics 23 (IBM Corp., Armonk, NY, USA). Analysis of variance (ANOVA) and Duncan\u0026rsquo;s test were used to evaluate significant differences between samples (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Data standardisation and principal component analysis (PCA) were performed in Origin 2019b, and OPLS-DA score plots, metabolite heatmaps, difference scatter plots, and weighted network plots were created using the omicshare online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicshare.come/tools/Home/Soft/get\u003c/span\u003e\u003cspan address=\"https://www.omicshare.come/tools/Home/Soft/get\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Soft).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Changes in leaf colour parameters\u003c/h2\u003e \u003cp\u003eLeaf colour parameters changed during the development of \u003cem\u003eQ. mongolica\u003c/em\u003e leaves, with L* showing a significant decrease followed by a significant increase and finally levelling off with leaf development. There were no significant differences in leaf colour parameters among stages S1, S5, and S6; however, they were significantly higher than those in stages S2, S3, and S4. This result indicates that leaf brightness was higher in young, colour transition, and senescence stages than during the green leaf stage. The leaf colour parameter a* displayed a significant increasing trend with continuous development of the leaf blade, and only the difference between stages S3 and S4 was not significant, indicating that the colour of the leaf blade changed from green to red. The leaf colour parameter b* initially displayed a significant decrease and then a significant increase with leaf blade development, and was significantly higher in the young leaf, colour change, and senescence stages than in the green leaf stage, and significantly higher in the green leaf stage than in stages S2, S3, and S4, which indicated that leaf blades turned yellow during the colour change and senescence stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in leaf pigment content\u003c/h2\u003e \u003cp\u003eChlorophyll a and chlorophyll b contents in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves displayed an increasing and then decreasing trend over time. Carotenoid content displayed a trend of decreasing and then increasing over time. In the six stages, the values of total the carotenoids/chlorophyll ratio were 0.22, 0.13, 0.13, 0.14, 0.19, and 0.29, respectively, and the proportion of carotenoids first decreased and then increased continuously. Anthocyanin glycoside content in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves displayed a significant increasing trend over time. Overall, anthocyanin glycoside content in leaves displayed a significant increasing trend during development, chlorophyll content increased and then decreased, and carotenoid content displayed a decreasing and then a significant increasing trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Anthocyanin metabolites in leaves\u003c/h2\u003e \u003cp\u003eThe colour of \u003cem\u003eQ. mongolica\u003c/em\u003e leaves gradually changed from green to deep red over the different developmental stages. For a deeper understanding of the differences in anthocyanin biosynthesis, we screened leaf samples from four stages (S1, S2, S5, and S6) for anthocyanin-targeted metabolome analysis based on the transcriptome results. Differences in anthocyanin metabolite contents in the leaves of stages S1, S2, S5, and S6 were evaluated using ultra-performance quadrupole\u0026ndash;linear ion trap liquid chromatography\u0026ndash;tandem mass spectrometry (UPLC/QTRAP-MS/MS). The results showed that total anthocyanin content increased gradually during leaf development. Total anthocyanin content was significantly lower in stages S1, S2, and S5 than in stage S6. A total of 56 metabolites were detected in \u003cem\u003eQ. mongolica\u003c/em\u003e leaf samples (Fig.\u0026nbsp;3a), which were categorised into eight groups: cyanidins (12), delphinidins (10), malvidins (5), pelargonidins (5), peonidins (7), petunidins (7), proanthocyanidins (2), and flavonoids (8). The main components of anthocyanin metabolites during stage S6 were cyanidin-3-O-glucoside, pelargonidin-3-O-glucoside, cyanidin-3,5-O-diglucoside, and cyanidin-3-O-sophoroside. Overall, total anthocyanin metabolite content within \u003cem\u003eQ. mongolica\u003c/em\u003e leaves displayed an increasing trend as the plant continued to develop. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was performed on the leaves (Fig.\u0026nbsp;3b). The OPLS-DA scoring plot showed clear separation between the developmental stages. The T score was 47.2% and the orthogonal T score was 13.9%. Segregation of anthocyanins between stages S1, S2, S5, and S6 indicated differences among leaf anthocyanin types and levels in the four developmental stages.\u003c/p\u003e \u003cp\u003eA combined multivariate statistical analysis of variable importance in projection (VIP) and fold change (FC) values of the OPLS-DA results was conducted to screen for differential metabolites between the comparison groups, based on thresholds of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and FC\u0026thinsp;\u0026ge;\u0026thinsp;2 and FC\u0026thinsp;\u0026le;\u0026thinsp;0.5. We compared groups S1, S2, and S5 with group S6. The S1 vs. S6 comparison had 40 differentially accumulated metabolites (DAMs) (29 upregulated, 11 downregulated), including 10 cyanidins, 5 delphinidins, 3 malvidins, 4 pelargonidins, 7 peonidins, 4 petunidins, 2 proanthocyanidins, and 5 flavonoids. The S2 vs. S6 comparison had 28 DAMs (25 upregulated, 3 downregulated), including 10 cyanidins, 4 delphinidins, 1 malvidin, 4 geraniolins, 3 pelargonidins, 1 petunidin, and 5 flavonoids. The S5 vs. S6 comparison had 22 DAMs (19 upregulated, 3 downregulated), including 10 cyanidin, 2 delphinidins, 1 malvidin, 3 pelargonidins, 2 peonidins, 1 petunidin, and 3 flavonoids (Supplementary Table\u0026nbsp;1). Differential metabolites common or unique to the three comparison groups (S1 vs. S6, S2 vs. S6, and S5 vs. S6) were visualised by a Venn analysis, with 46 differential metabolites shared among the three groups, and 15, 4, and 0 metabolites unique to the three groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). A heatmap of the differential metabolites was created to visualise the accumulation of differential metabolites in the comparative groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The results suggested that upregulated expression of anthocyanin metabolites occurred in plant leaves during autumn.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;3. (a) Heatmap of all metabolites within a sample. (b) Orthogonal partial least-squares discriminant analysis scoring plot, where the horizontal and vertical axes indicate predicted orthogonal principal components, respectively, and horizontal and vertical distances indicate gaps between and within groups, respectively. Percentage indicates how well each component explains the dataset. Each point in the graph represents a sample, and samples from the same group are represented using the same colour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Transcriptomic analysis during leaf development\u003c/h2\u003e \u003cp\u003eTo determine the molecular mechanism of anthocyanin synthesis in leaves, we performed transcriptome analyses at six developmental stages to identify differential genes. A total of 115.33 GB of clean data was generated. The clean data of each library ranged from 5653766953 to 7489395393 bp, with an average score of 92.66\u0026ndash;93.76% for Q30 bases. After filtering out uncertain reads, aptamer-related reads, and low-quality reads, we obtained clean data from the raw RNA sequencing (RNA-seq) data. These high-quality data provided assurance for further gene expression analyses. Based on thresholds of |log2(FC)| \u0026gt; 2 and false detection rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we analysed differences among the 15 comparison groups. The results showed that among 15 permutations, there were large differences among the following comparison groups: S1 vs. S2, S1 vs. S4, S1 vs. S5, S1 vs. S6, S2 vs. S6, S3 vs. S6, S4 vs. S6, and S5 vs. S6. To investigate anthocyanin changes in \u003cem\u003eQ. mongolica\u003c/em\u003e during different developmental stages, we analysed five comparison groups (S1 vs. S6, S2 vs. S6, S3 vs. S6, S4 vs. S6, and S5 vs. S6), and identified 14,688 differentially expressed genes (DEGs) (4,085 upregulated, 10,603 downregulated), 12,735 DEGs (3,594 upregulated, 9,141 downregulated), 12,618 DEGs (3,473 upregulated, 9,145 downregulated), 11,994 DEGs (1,609 upregulated, 4,151 downregulated), and 9,755 DEGs (2,942 upregulated, 6,813 downregulated), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKyoto Encyclopaedia of Genes and Genomes (KEGG) analysis of DEGs at different stages revealed that metabolic pathways contributing to anthocyanin accumulation in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves, such as the biosynthesis of secondary metabolites, starch and sucrose metabolism, and photosynthesis, were significantly enriched in the different comparison groups (Supplementary Fig.\u0026nbsp;1). Metabolic pathways closely related to anthocyanin biosynthesis were annotated; these pathways included the phenylpropanoid (ko00940), flavonoid (ko00941), anthocyanidin (ko00942), isoflavonoid (ko00943), and flavonoid and flavonol (ko00944) biosynthesis pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further understand the gene regulatory network and determine the genes affecting leaf colour changes in \u003cem\u003eQ. mongolica\u003c/em\u003e, we performed weighted gene co-expression network analysis (WGCNA). Genes with fragments per kilobase of transcript per million mapped reads (FPKM)\u0026thinsp;\u0026gt;\u0026thinsp;5 (15,220 genes) were used as source data to construct a scale-free co-expression network based on the β\u0026thinsp;=\u0026thinsp;14 soft-threshold power value. Based on the WGCNA results, clusters of genes with high degrees of interconnectivity were defined as modules, where genes within the same module had high correlation coefficients. A total of 20 modules were identified using the dynamic tree-cutting method (mergeCutHeight\u0026thinsp;=\u0026thinsp;0.2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The number of genes per module ranged from 31 to 2,674; the grey module was a collection of genes not assigned to other modules (31 genes). Correlation analysis of module eigenvalues and trait data showed that the blue, orange, dark grey, green, plum, midnight blue, yellow-green, and grey modules were highly significantly positively or negatively correlated with physiological indicators (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Correlation averages of genes within each module were obtained by calculating the correlation between gene and trait data and analysing the association of each module with the traits. The critical threshold for assigning gene significance (GS) was \u0026ge;\u0026thinsp;0.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), and among the highly significantly correlated modules, blue (2,179 genes, r\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), green (2,422 genes, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), plum (866 genes, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.73, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and yellow-green (103 genes, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.77, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were highly significantly associated with anthocyanins. The blue module (2,179 genes, r\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was highly significantly positively correlated with a*, and the plum (866 genes, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and yellow-green modules (103 genes, r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were highly significantly correlated with a*.\u003c/p\u003e \u003cp\u003eIn the blue module, genes were significantly enriched in the autophagy pathway. In the green module, genes were significantly enriched in the ribosomal pathway. In the plum module, genes were significantly enriched in the GPI-anchored protein biosynthesis pathway, RNA degradation, and spliceosome pathway. In the yellow-green module, genes were significantly enriched in the basic excision repair, DNA replication, and lipoic acid metabolism pathways. Thus, genes showing module membership (MM)\u0026thinsp;\u0026gt;\u0026thinsp;0.9 and GS\u0026thinsp;\u0026gt;\u0026thinsp;0.6 in the blue module were defined as central genes. We screened a total of 1,367 and 2,179 central genes related to anthocyanin synthesis and a*, respectively, and constructed a gene network diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea, b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo comprehensively analyse the pathways affecting anthocyanin synthesis during leaf colour changes in \u003cem\u003eQ. mongolica\u003c/em\u003e, we performed gene set enrichment analysis (GSEA) and found that the ko00943 pathway related to anthocyanin synthesis satisfied the condition of FDR (q)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and was therefore significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Differential expression of structural genes for anthocyanin biosynthesis\u003c/h2\u003e \u003cp\u003eBased on the KEGG, WGCNA, and GSEA analysis results, we identified 22 candidate genes involved in the anthocyanin biosynthesis pathway that showed high transcriptional activity in at least one leaf developmental stage, with high expression levels (FPKM\u0026thinsp;\u0026gt;\u0026thinsp;10.0). These putative structural genes included \u003cem\u003eQmPAL\u003c/em\u003e (4), \u003cem\u003eQm4CL\u003c/em\u003e (8), \u003cem\u003eQmCHS\u003c/em\u003e (2), \u003cem\u003eQmCHI\u003c/em\u003e (2), \u003cem\u003eQmDFR\u003c/em\u003e (1), \u003cem\u003eQmFLS\u003c/em\u003e (1), \u003cem\u003eQmLAR\u003c/em\u003e (1), \u003cem\u003eQmANS\u003c/em\u003e (1), \u003cem\u003eQmUFGT\u003c/em\u003e (1), and \u003cem\u003eQmCYP81E8\u003c/em\u003e (1) (Supplementary Table\u0026nbsp;2). A comparison of the six stages revealed that all 22 candidate genes were DEGs. Each DEG had different expression patterns at the six developmental stages. Next, we examined DEGs that were expressed at extremely high levels (FPKM\u0026thinsp;\u0026gt;\u0026thinsp;10) in at least one leaf developmental stage. A trend analysis of the expression of the 22 candidate genes is shown in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Correlation analysis of anthocyanin biosynthesis-related DEGs and DAMs\u003c/h2\u003e \u003cp\u003eTo identify candidate regulatory genes involved in anthocyanin biosynthesis in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves, we performed correlation analysis of the 22 DEGs and 32 DAMs that were differentially expressed in stages S1, S2, S5, and S6 (Supplementary Fig.\u0026nbsp;3). We found that 12 differentially expressed anthocyanin structural genes were significantly correlated with 27 anthocyanins (|PCC| \u0026gt; 0.9, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01); their correlation coefficient weight network plots are shown in Fig.\u0026nbsp;9. These results suggest that these 12 structural genes may be influence anthocyanin biosynthesis in leaves. The relative expression of the four major anthocyanin metabolites pelargonidin-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside and cyanidin-3,5-O-diglucoside in stage S6 were highly significantly and positively correlated with \u003cem\u003eQmCHS1\u003c/em\u003e (gene-Qm025027), \u003cem\u003eQmANS\u003c/em\u003e (gene-Qm031942), \u003cem\u003eQmCYP81E8\u003c/em\u003e (gene-Qm029525), and \u003cem\u003eQm4CL3\u003c/em\u003e (gene-Qm020350) expression, respectively. These four genes were hypothesised to be the key genes affecting biosynthesis of the major anthocyanins in leaves. We mapped the structural genes and anthocyanin metabolite pathways that were differentially expressed in the anthocyanin biosynthesis pathway during leaf development (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The expression levels of one \u003cem\u003eQmCHS\u003c/em\u003e gene (gene-Qm025027) and one \u003cem\u003eQmANS\u003c/em\u003e (gene-Qm031942) gene increased with leaf development and were more than 16-fold higher at S6 than at S1. This finding was consistent with the significantly higher cyanidin-3-O-glucoside and pelargonidin-3-O-glucoside contents during S6 than during S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Correlation analysis of DEGs encoding TFs and differential anthocyanin metabolites\u003c/h2\u003e \u003cp\u003eTo further identify candidate TFs involved in the regulation of anthocyanin biosynthesis in leaves, we analysed the MYB, bHLH, WRKY, NAC, bZIP, and HSF TFs, which were significantly differentially expressed at each development stage. We examined TFs with high expression levels in at least one leaf developmental stage (FPKM\u0026thinsp;\u0026gt;\u0026thinsp;50) and screened 87 TFs, including 13 MYB, 13 bHLH, 19 WRKY, 22 NAC, 13 bZIP, and seven HSFTFs. We created a heatmap of the correlation between anthocyanin biosynthesis genes and 87 differential TFs, and selected a total of 12 anthocyanin biosynthesis genes in seven categories (\u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e, \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCYP81E8\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, and \u003cem\u003eANS\u003c/em\u003e) that were previously screened as guide genes for correlation analysis (Supplementary Fig.\u0026nbsp;4). Among the differentially expressed TFs, 30 were highly significantly correlated with nine structural genes. Of these, 25 (5 MYB, 5 bHLH, 2 WRKY, 10 NAC, and 3 bZIP) TFs were highly significantly positively correlated with anthocyanin biosynthesis genes (|PCC| \u0026gt; 0.9, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Five TFs (1 bHLH, 3 WRKY, and 2 NAC) were highly significantly negatively correlated with anthocyanin biosynthesis genes (|PCC| \u0026gt; 0.9, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Their weight network diagrams are shown in Fig.\u0026nbsp;11. In addition, correlation analysis of the differentially expressed TFs with \u003cem\u003eQmCHS1\u003c/em\u003e (gene-Qm025027), \u003cem\u003eQmANS\u003c/em\u003e (gene-Qm031942), \u003cem\u003eQmCYP81E8\u003c/em\u003e (gene-Qm029525), and \u003cem\u003eQm4CL3\u003c/em\u003e (gene-Qm020350) showed that 1 bHLH, 3 bZIP, 1 MYB, 10 NAC, and tw2o WRKY TFs played strong facilitating roles in regulating anthocyanin structural genes (Supplemental Table\u0026nbsp;3). The expression levels of \u003cem\u003eQmNAC047\u003c/em\u003e, \u003cem\u003eQmNAC056\u003c/em\u003e, \u003cem\u003eQmNAC087\u003c/em\u003e, and \u003cem\u003eQm NAP1\u003c/em\u003e fluctuated slightly during stages S1\u0026ndash;S4 and were extremely highly elevated in S5 and S6; their expression levels during S6 were 162-, 446-, 381-, and 1,812-fold higher than that during S1, respectively (Fig.\u0026nbsp;12), which suggests that \u003cem\u003eQmNAC\u003c/em\u003e plays a major role in anthocyanin biosynthesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;11. Weighted network diagram of correlation coefficients between anthocyanin structural genes and transcription factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Validation of key DEGs involved in anthocyanin accumulation by quantitative reverse-transcription polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTo further confirm the reliability of the RNA-seq data, we performed qRT- PCR analysis of the four structural genes and five TFs involved in the anthocyanin biosynthesis pathway, and assessed differences in the transcript levels of a number of putative genes associated with anthocyanin accumulation. The results showed similar expression trends between the RNA-seq data and qRT-PCR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). Linear regression analysis showed significant positive correlation between the RNA-seq and qRT-PCR data (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e13\u003c/span\u003eb), indicating the accuracy and reliability of the RNA-seq data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe L*, a*, and b* leaf colour parameters express leaf colour in a precise, quantitative manner, with increasing L* values indicating a shift from dark to bright, a* values from negative to positive indicating a shift from green to red, and b* values from negative to positive indicating a shift from blue to yellow. In this study, the most variable leaf colour parameter was a*, which was negative in stages S1\u0026thinsp;\u0026minus;\u0026thinsp;S4, and positive and increasing in stages S5 and S6. These results indicate that leaves changed from green to red during the period from S4 to S5. L* displayed an initially decreasing and then increasing trend. During S1, the leaf blades were young and bright, light green in colour. During S2\u0026ndash;S4, leaf blades gradually turned dark green. During S5 and S6, leaf blades gradually turned red, with increased brightness. The b* values initially displayed a decreasing trend followed by an increasing trend, such that the leaf blades became more yellow during the later stages of development.\u003c/p\u003e \u003cp\u003eAs the leaves developed, their anthocyanin content increased continuously, becoming significantly higher during S5 and S6, indicating the rapid accumulation of anthocyanins in autumn, when the leaves turned red. Chlorophyll content increased and then decreased, indicating its continuous accumulation during leaf development, reaching a maximum in mature leaves. Chlorophyll declined in autumn, and anthocyanins accumulated rapidly, which caused the leaves to turn red. Carotenoid content initially decreased and then increased, reaching significantly higher levels during S6, indicating that carotenoids accumulated significantly in the late stage of leaf development.\u003c/p\u003e \u003cp\u003eAnthocyanins are a class of water-soluble pigments in the vesicles of plant epidermal cells that impart a wide range of colours to plant organs and tissues. The accumulation of anthocyanins in plant organs is a key trait affecting plant quality and ornamental value [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, we integrated metabolomic and transcriptomic analyses to identify the anthocyanin components and genes involved in anthocyanin biosynthesis during leaf development. In recent years, the anthocyanin contents and fractions of different plant leaves have been reported. For example, the major anthocyanins in the zikui (\u003cem\u003eCamellia sinensis\u003c/em\u003e) tea tree are petunidin-3-O-glucoside, cyanidin-3-O-galactoside, and cyanidin-3-O-glucoside [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies conducted on \u003cem\u003eQuercus aliena\u003c/em\u003e leaves revealed that the accumulation of anthocyanins such as cyanidin-3-O-glucoside and cyanidin-3-O-sambubiglycoside had significant effects on leaf colouration [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In \u003cem\u003eacer triflorum\u003c/em\u003e, cyanidin-3-O-arabinoside is closely related to red leaf colour [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The major anthocyanin in the leaves of \u003cem\u003ePhoebe bournei\u003c/em\u003e is cyanidin-3-O-glucoside [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on anthocyanin-targeted metabolome analysis, we further identified 48 anthocyanins differential metabolites and found that pelargonidin-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside, and cyanidin-3,5-O-diglucoside were the major anthocyanins in red leaves of \u003cem\u003eQ. mongolica\u003c/em\u003e. By analysing the anthocyanin fractions of developing leaves, we found that the contents of most delphinidin, malvidin, peonidin, and petunidin derivatives were extremely low or decreased with the prolongation of each developmental stage. This result suggests that the delphinidin, malvidin, peonidin, and petunidin fractions were not the major factors driving leaf colour changes. Additionally, pelargonidin-3-O-glucoside, cyanidin-3,5-O-diglucoside, cyanidin-3-O-glucoside, and cyanidin-3-O-sophoroside contents tended to increase with developmental stage, and cyanidin-3-O-glucoside displayed a highly significant increasing trend, suggesting that high levels of blue pigment components in these derivatives may be essential for red leaf colour in \u003cem\u003eQ. mongolica\u003c/em\u003e; this finding is consistent with previous studies on ornamental plants.\u003c/p\u003e \u003cp\u003eThe regulatory mechanisms of colour formation are closely related to the expression of anthocyanin structural genes and TFs [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Anthocyanin biosynthesis is a branch of the flavonoid synthesis pathway starting from phenylalanine, and involving a variety of enzymes encoded by early biosynthesis genes (\u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e, \u003cem\u003eF3H\u003c/em\u003e, \u003cem\u003eF3\u0026rsquo;H\u003c/em\u003e, and \u003cem\u003eF3\u0026rsquo;5\u0026rsquo;H\u003c/em\u003e) and anthocyanin biosynthesis genes (\u003cem\u003eDFR\u003c/em\u003e, \u003cem\u003eANS\u003c/em\u003e, and \u003cem\u003eUFGT\u003c/em\u003e) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Previous studies have shown that several key genes and enzymes are required for anthocyanin accumulation and leaf colour formation in many ornamental plants, such as \u003cem\u003eQuercus dentata\u003c/em\u003e [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and cassava (\u003cem\u003eManihot esculenta\u003c/em\u003e) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe identified 22 DEGs associated with anthocyanin biosynthesis in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves, including 16 genes for early biosynthesis, one for proanthocyanidin synthesis, one for flavonol synthesis, one for isoflavone synthesis, and three for anthocyanin biosynthesis. The expression levels of \u003cem\u003eQmCYP81E8\u003c/em\u003e, \u003cem\u003eQm4CL\u003c/em\u003e, \u003cem\u003eQmCHS\u003c/em\u003e, \u003cem\u003eQmANS\u003c/em\u003e, \u003cem\u003eQmCHI\u003c/em\u003e, \u003cem\u003eQmFLS\u003c/em\u003e, \u003cem\u003eQmUFGT\u003c/em\u003e, and \u003cem\u003eQmDFR\u003c/em\u003e were significantly positively correlated with the anthocyanin content in this study. Among these, \u003cem\u003eQmANS\u003c/em\u003e, \u003cem\u003eQmCHS1\u003c/em\u003e, \u003cem\u003eQmCYP81E8\u003c/em\u003e, and \u003cem\u003eQm4CL3\u003c/em\u003e were strongly positively correlated with the four major colourants.\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eQmANS\u003c/em\u003e and \u003cem\u003eQmCHS1\u003c/em\u003e gene expression levels were significantly upregulated during S5 and S6. In the anthocyanidin synthesis pathway, anthocyanidin synthase (\u003cem\u003eANS\u003c/em\u003e) catalyses the formation of colourless anthocyanidin glycosides to construct colourful anthocyanidin glycosides, and this intermediate product is further coupled with 3-O-glucosyltransferase (3GT) and transported to vesicles to form colourful 3-O-glycosidised anthocyanosides [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], which play important roles in the formation of plant colour. In the peony \u003cem\u003ePaeonia suffruticosa\u003c/em\u003e, \u003cem\u003ePaANS\u003c/em\u003e is expressed at higher levels in purplish-red leaves than in yellow-green leaves [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Other studies have shown that the overexpression of \u003cem\u003eSmANS\u003c/em\u003e increases anthocyanin content in \u003cem\u003eSalvia officinalis\u003c/em\u003e, whereas low \u003cem\u003eSmANS\u003c/em\u003e expression in the flowers of \u003cem\u003eSalvia officinalis\u003c/em\u003e resulted in white colouration [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Upregulated \u003cem\u003eCsANS\u003c/em\u003e expression in the zikui tea tree (\u003cem\u003eCamellia sinensis\u003c/em\u003e cv. \u0026lsquo;Zikui\u0026rsquo;) may promote anthocyanin accumulation [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the flowers of a \u003cem\u003eCymbidium\u003c/em\u003e hybrid, the expression of \u003cem\u003eChANS\u003c/em\u003e and \u003cem\u003eChDFR\u003c/em\u003e was found to be closely related to anthocyanin accumulation patterns [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. \u003cem\u003eRsPAL\u003c/em\u003e, \u003cem\u003eRs4CL\u003c/em\u003e, \u003cem\u003eRsCHS\u003c/em\u003e, \u003cem\u003eRsCHI\u003c/em\u003e, and \u003cem\u003eRsDFR\u003c/em\u003e enzymes are more highly expressed in the red variant of radish (\u003cem\u003eRaphanus sativus\u003c/em\u003e) than in the white variant, and \u003cem\u003eRsCHS\u003c/em\u003e plays an important role in radish root colouration [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It has been suggested that \u003cem\u003eQaCHS1\u003c/em\u003e, \u003cem\u003eQaCHI\u003c/em\u003e, and \u003cem\u003eQaANS1\u003c/em\u003e may be the key regulators of \u003cem\u003eQuercus aliena\u003c/em\u003e anthocyanin synthesis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Studies on the safflower \u003cem\u003eCarthamus tinctorius\u003c/em\u003e have shown that transcriptional regulation of \u003cem\u003eCtCYP81E8\u003c/em\u003e is associated with flavonoid accumulation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Because \u003cem\u003eQmANS\u003c/em\u003e is an anthocyanin synthesis gene, it is hypothesised to be a key determinant of \u003cem\u003eQ. mongolica\u003c/em\u003e leaf reddening.\u003c/p\u003e \u003cp\u003eIn addition to structural genes, TFs play important roles in the regulation of anthocyanin biosynthesis. We analysed MYB, bHLH, WRKY, bZIPs, NAC, and HSF. Previous studies have shown that MYB-bHLH-WD40 is a major class of TFs regulating structural genes of the anthocyanin pathway, as has been reported in Arabidopsis [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], grape [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], persimmon [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and strawberry [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. It has also been reported that the R2R3-MYB gene controls the expression of the key genes CHS, CHI, and F3\u0026rsquo;H in anthocyanin biosynthesis.\u003c/p\u003e \u003cp\u003eIn this study, one bHLH, three bZIP, one MYB, 10 NAC, and two WRKY TFs were strongly positively correlated with the expression of structural genes, and were identified from the DEG data as candidate TFs. We then performed a phylogenetic analysis of the predicted protein sequences of five candidate MYB transcription factors as well as MYB gene family proteins from Arabidopsis (Supplementary Fig.\u0026nbsp;5). The phylogenetic tree showed that the TF \u003cem\u003eQmSRM1\u003c/em\u003e belongs to the same branch as \u003cem\u003eAtMYB1\u003c/em\u003e, \u003cem\u003eAtMYB43\u003c/em\u003e, and \u003cem\u003eAtMYB78\u003c/em\u003e in Arabidopsis. The R2R3-MYB TF \u003cem\u003eRcMYB1\u003c/em\u003e plays a central role in the biosynthesis of rose anthocyanins [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, we hypothesise that \u003cem\u003eQmSRM1\u003c/em\u003e may be involved in the synthesis and accumulation of anthocyanin.\u003c/p\u003e \u003cp\u003eNAC TFs also play a role in regulating anthocyanin accumulation. We performed a phylogenetic analysis of the predicted protein sequences of seven candidate NAC TFs and NAC gene family proteins from Arabidopsis (Supplementary Fig.\u0026nbsp;6). The phylogenetic tree showed that QmNAC087 belongs to the same branch as AtNAC087 in Arabidopsis. In Arabidopsis, \u003cem\u003eAtNAC087\u003c/em\u003e overexpression induces anthocyanin accumulation, decreases chlorophyll content, and enhances the expression of leaf senescence-related genes. The expression of \u003cem\u003eAfNAC083\u003c/em\u003e is positively correlated with the anthocyanin content in \u003cem\u003eAcer fabri\u003c/em\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eIbNAC056a/b\u003c/em\u003e in sweet potato promotes anthocyanin accumulation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. \u003cem\u003eIbNAC056\u003c/em\u003e, \u003cem\u003eQmNAC056\u003c/em\u003e, and \u003cem\u003eQmNAC047\u003c/em\u003e were clustered in the same branch as \u003cem\u003eAtNAC056\u003c/em\u003e, and it has been hypothesised that \u003cem\u003eQmNAC056\u003c/em\u003e and \u003cem\u003eQmNAC047\u003c/em\u003e promote anthocyanin accumulation. In this study, \u003cem\u003eQmNAC073\u003c/em\u003e, \u003cem\u003eQmNAP1\u003c/em\u003e, and \u003cem\u003eQmJA2L\u003c/em\u003e were positively correlated with anthocyanin accumulation, and were hypothesised to be involved in anthocyanin synthesis regulation. Overexpression of \u003cem\u003eDcNAP1\u003c/em\u003e promotes leaf senescence in \u003cem\u003eDianthus caryophyllus\u003c/em\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and \u003cem\u003ePanicum virgatum\u003c/em\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. We hypothesised that this process is related to anthocyanin accumulation, which requires further investigation in future studies.\u003c/p\u003e \u003cp\u003eOther TFs also play roles in regulating anthocyanin accumulation. This study showed that \u003cem\u003eQmWRKY72A\u003c/em\u003e, \u003cem\u003eQmWRKY75\u003c/em\u003e, \u003cem\u003eQmILR3\u003c/em\u003e (bHLH), \u003cem\u003eQmbZIP53\u003c/em\u003e, and \u003cem\u003eQmCPRF2\u003c/em\u003e (bZIP) were significantly positively correlated with structural genes. \u003cem\u003eMdWRKY72\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and \u003cem\u003eMdWRKY75\u003c/em\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] have been shown to regulate anthocyanin synthesis in apple fruit. According to protein interactions, ILR3 is an important bHLH interacting with the MYB, RGA, CRY1, and bZIP proteins, which regulate anthocyanin biosynthesis during the sesame life cycle [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. We hypothesised that \u003cem\u003eQmWRKY72A\u003c/em\u003e, \u003cem\u003eQmWRKY75\u003c/em\u003e, and \u003cem\u003eQmILR3\u003c/em\u003e (bHLH) regulate anthocyanin accumulation in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves. Chrysanthemum flowers have been shown to become lighter in colour as anthocyanin levels decrease and bZIP53 expression is upregulated [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which is inconsistent with the present findings. The phosphorylation state of CPRF2 (bZIP), which is a phosphorylated protein, increases rapidly in response to light [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. CPRF2 (bZIP) is negatively correlated with anthocyanin accumulation in \u003cem\u003eDendrobium chrysotoxum\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], which is inconsistent with the results of the present study; therefore, we hypothesise that the leaves of \u003cem\u003eD. chrysotoxum\u003c/em\u003e and \u003cem\u003eQ. mongolica\u003c/em\u003e have different colouration mechanisms.\u003c/p\u003e \u003cp\u003eOur TF expression analyses showed that the expression of NAC TFs was significantly higher than that of other TFs during phases S5 and S6. The expression levels of \u003cem\u003eQmNAC047\u003c/em\u003e, \u003cem\u003eQmNAC056\u003c/em\u003e, \u003cem\u003eQmNAC087\u003c/em\u003e, and \u003cem\u003eQmNAP1\u003c/em\u003e fluctuated slightly during S1\u0026ndash;S4 and were extremely elevated during S5 and S6. Expression levels at S6 were 162, 446, 381, and 1812 times higher than those at S1, respectively. Therefore, we hypothesise that \u003cem\u003eQmNAC047\u003c/em\u003e, \u003cem\u003eQmNAC056\u003c/em\u003e, \u003cem\u003eQmNAC087\u003c/em\u003e, and \u003cem\u003eQmNAP1\u003c/em\u003e play key regulatory roles in anthocyanin synthesis.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this study, we present detailed potential pathways for anthocyanin biosynthesis. Our findings demonstrate that pelargonidinl-3-O-glucoside, cyanidin-3-O-sophoroside, cyanidin-3-O-glucoside, and cyanidin-3,5-O-diglucoside are the main colour-presenting substances in red leaves of \u003cem\u003eQ. mongolica\u003c/em\u003e. In the anthocyanin biosynthesis pathway, a total of 12 structural genes and 17 TFs were found to be involved in the regulation of anthocyanin biosynthesis, and \u003cem\u003eQmANS\u003c/em\u003e, \u003cem\u003eQmNAC047\u003c/em\u003e, \u003cem\u003eQmNAC056\u003c/em\u003e, \u003cem\u003eQmNAC087\u003c/em\u003e, and \u003cem\u003eQmNAP1\u003c/em\u003e were found to be the likely key determinants of anthocyanin biosynthesis. Further studies of the precise functional expression mechanisms of the transgenes are needed to validate their role in the regulation of anthocyanin biosynthesis.\u003c/p\u003e \u003cp\u003eThe English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.textcheck.com/certificate/ty7v5N\u003c/span\u003e\u003cspan address=\"http://www.textcheck.com/certificate/ty7v5N\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper and its Supporting information files.\u0026nbsp;The datasets generated or analysed during the current study are available in the NCBI repository, PRJNA1048709.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003ecknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all the members of the research group for their helpful comments and inspiration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003enterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared\u0026nbsp;that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.Y. and D.H.; methodology, Y.Y. and W.H.; validation, X.L.,B.S., and Q.W.; resources, J.L. and Z.Z.; writing\u0026mdash;original draft preparation, Y.Y.,and J.P.; writing\u0026mdash;review and editing, Y.Y.; supervision, H.C. and J.D.; project administration, J.Z.,M.Z., and H.L.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant Material Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant material we use complies with relevant institutional, national and international standards and legislation.\u003c/p\u003e\n\u003cp\u003eWe collected \u003cem\u003eQ.Mongolica\u003c/em\u003e leaves in Yixian, Baoding, Hebei Province, China. 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STRONG STAYGREEN inhibits DNA binding of PvNAP transcription factors during leaf senescence in switchgrass. Plant Physiol. 190, 2045\u0026ndash;2058(2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, M. \u003cem\u003eet al\u003c/em\u003e. The WKRY transcription factor MdWRKY75 regulates anthocyanins accumulation in apples (\u003cem\u003eMalus domestica\u003c/em\u003e). Funct Plant Biol. 49, 799\u0026ndash;809(2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeyed, K. K., Sahar, F. \u0026amp; Hamid, N. Z. Identification and in silico evaluation of bHLH genes in the Sesamum indicum genome: Growth regulation and stress dealing specially through the metal ions homeostasis and flavonoid biosynthesis. Gene Rep. 19, 100639(2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, Z. \u003cem\u003eet al\u003c/em\u003e. Transcriptome analysis reveals chrysanthemum flower discoloration under high-temperature stress. 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J Plant Biol. 66, 359\u0026ndash;371(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Quercus mongolica, Leaf colour, Anthocyanin, Transcriptomics, Metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-3845207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3845207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eQuercus mongolica\u003c/em\u003e is a tall tree with a broad, rounded crown and lush leaves. In autumn, the leaves turn red and have great ornamental value. However, the molecular mechanisms that cause the change in leaf colour are unknown. In this study, we identified 12 differentially expressed genes involved in anthocyanin synthesis by analysing the transcriptome of \u003cem\u003eQ. mongolica\u003c/em\u003e leaves in six developmental stages (S1\u0026thinsp;\u0026minus;\u0026thinsp;S6). We further analysed the dynamics of anthocyanin content in \u003cem\u003eQ. mongolica\u003c/em\u003e leaves in four developmental stages (S1, S2, S5, and S6) using differential gene expression patterns. We detected a total of 48 anthocyanins and categorised these into seven major anthocyanin ligands. The most abundant anthocyanins in the red leaves of \u003cem\u003eQ. mongolica\u003c/em\u003e were cyanidin-3,5-O-diglucoside, cyanidin-3-O-glucoside, cyanidin-3-O-sophoroside, and pelargonidin-3-O-glucoside. Correlation analysis of differentially expressed genes and anthocyanin content identified highly expressed \u003cem\u003eQmANS\u003c/em\u003e as a key structural gene associated with anthocyanin biosynthesis in \u003cem\u003eQ. mongolica\u003c/em\u003e. A transcription factor-structural gene correlation analysis showed that the 1bHLH, 3bZIP, 1MYB, 10NAC, and 2WRKY transcription factors played strong positive roles in regulating anthocyanin structural genes (|PCC| \u0026gt; 0.90), with the \u003cem\u003eQmNAC\u003c/em\u003e transcription factor playing a major role in anthocyanin biosynthesis.\u003c/p\u003e","manuscriptTitle":"Integrated transcriptomics and metabolomics analyses provide insights into anthocyanin biosynthesis for leaf colour formation in Quercus mongolica","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 10:06:03","doi":"10.21203/rs.3.rs-3845207/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9ad601f-5a77-480c-9ea4-bffb8d2f93e9","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28192788,"name":"Biological sciences/Biological techniques/Metabolomics"},{"id":28192789,"name":"Biological sciences/Biological techniques/Sequencing/Rna sequencing"},{"id":28192790,"name":"Biological sciences/Plant sciences/Plant molecular biology"},{"id":28192791,"name":"Biological sciences/Genetics/Gene expression"},{"id":28192792,"name":"Biological sciences/Genetics/Gene regulation"},{"id":28192793,"name":"Biological sciences/Genetics/Genomics"},{"id":28192794,"name":"Biological sciences/Genetics/Plant genetics"},{"id":28192795,"name":"Biological sciences/Genetics/Sequencing"},{"id":28192796,"name":"Biological sciences/Plant sciences/Plant physiology"}],"tags":[],"updatedAt":"2024-10-22T06:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 10:06:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3845207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3845207","identity":"rs-3845207","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}