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Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers

  • # Authors contributed equally: Renjuan Qian, Youju Ye

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  • Clematis lanuginosa, a valuable ornamental plant in Zhejiang Province, China, produces flowers that are blue–purple, a rare flower colour. In this study, to explore the anthocyanin synthesis mechanism involved in flower colour formation in C. lanuginosa, metabolome, and transcriptome sequencing was performed at six flower development stages. Metabolome analysis revealed 25 anthocyanin compounds and 22 differentially expressed metabolites. Cyanidin-3,5-O-diglucoside, cyanidin-3,5,3'-O-triglucoside, cyanidin-3-O-rutinoside-5-O-glucoside, delphinidin-3-O-glucoside, and petunidin-3-O-glucoside may promote the formation of blue–purple colour in flowers. The combined analysis results revealed that the transcript41913_f2p0_1152 gene (MYB-like) may be a key gene in C. lanuginosa blue–purple flower colour development. These results provide the basis for further research on the blue–purple flower colour of C. lanuginosa.
  • As a major staple crop, today maize accounts for approximately 40% of total worldwide cereal production (http://faostat.fao.org/). Since its domestication ~9,000 years ago from a subgroup of teosinte (Zea mays ssp. parviglumis) in the tropical lowlands of southwest Mexico[1], its cultivating area has greatly expanded, covering most of the world[2]. Human's breeding and utilization of maize have gone through several stages, from landraces, open-pollinated varieties (OPVs), double-cross hybrids (1930s-1950s) and since the middle 1950s, single-cross hybrids. Nowadays, global maize production is mostly provided by single-cross hybrids, which exhibit higher-yielding and better stress tolerance than OPVs and double-cross hybrids[3].

    Besides its agronomic importance, maize has also been used as a model plant species for genetic studies due to its out-crossing habit, large quantities of seeds produced and the availability of diverse germplasm. The abundant mutants of maize facilitated the development of the first genetic and cytogenetic maps of plants, and made it an ideal plant species to identify regulators of developmental processes[46]. Although initially lagging behind other model plant species (such as Arabidopsis and rice) in multi-omics research, the recent rapid development in sequencing and transformation technologies, and various new tools (such as CRISPR technologies, double haploids etc.) are repositioning maize research at the frontiers of plant research, and surely, it will continue to reveal fundamental insights into plant biology, as well as to accelerate molecular breeding for this vitally important crop[7, 8].

    During domestication from teosinte to maize, a number of distinguishing morphological and physiological changes occurred, including increased apical dominance, reduced glumes, suppression of ear prolificacy, increase in kernel row number, loss of seed shattering, nutritional changes etc.[9] (Fig. 1). At the genomic level, genome-wide genetic diversity was reduced due to a population bottleneck effect, accompanied by directional selection at specific genomic regions underlying agronomically important traits. Over a century ago, Beadle initially proposed that four or five genes or blocks of genes might be responsible for much of the phenotypic changes between maize and teosinte[10,11]. Later studies by Doebley et al. used teosinte–maize F2 populations to dissect several quantitative trait loci (QTL) to the responsible genes (such as tb1 and tga1)[12,13]. On the other hand, based on analysis of single-nucleotide polymorphisms (SNPs) in 774 genes, Wright et al.[14] estimated that 2%−4% of maize genes (~800−1,700 genes genome-wide) were selected during maize domestication and subsequent improvement. Taking advantage of the next-generation sequencing (NGS) technologies, Hufford et al.[15] conducted resequencing analysis of a set of wild relatives, landraces and improved maize varieties, and identified ~500 selective genomic regions during maize domestication. In a recent study, Xu et al.[16] conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accession. They identified 394 domestication sweeps and 360 adaptation sweeps. Collectively, these studies suggest that maize domestication likely involved hundreds of genomic regions. Nevertheless, much fewer domestication genes have been functionally studied so far.

    Figure 1.  Main traits of maize involved in domestication and improvement.

    During maize domestication, a most profound morphological change is an increase in apical dominance, transforming a multi-branched plant architecture in teosinte to a single stalked plant (terminated by a tassel) in maize. The tillers and long branches of teosinte are terminated by tassels and bear many small ears. Similarly, the single maize stalk bears few ears and is terminated by a tassel[9,12,17]. A series of landmark studies by Doebley et al. elegantly demonstrated that tb1, which encodes a TCP transcription factor, is responsible for this transformation[18, 19]. Later studies showed that insertion of a Hopscotch transposon located ~60 kb upstream of tb1 enhances the expression of tb1 in maize, thereby repressing branch outgrowth[20, 21]. Through ChIP-seq and RNA-seq analyses, Dong et al.[22] demonstrated that tb1 acts to regulate multiple phytohormone signaling pathways (gibberellins, abscisic acid and jasmonic acid) and sugar sensing. Moreover, several other domestication loci, including teosinte glume architecture1 (tga1), prol1.1/grassy tillers1, were identified as its putative targets. Elucidating the precise regulatory mechanisms of these loci and signaling pathways will be an interesting and rewarding area of future research. Also worth noting, studies showed that tb1 and its homologous genes in Arabidopsis (Branched1 or BRC1) and rice (FINE CULM1 or FC1) play a conserved role in repressing the outgrowth of axillary branches in both dicotyledon and monocotyledon plants[23, 24].

    Teosinte ears possess two ranks of fruitcase-enclosed kernels, while maize produces hundreds of naked kernels on the ear[13]. tga1, which encodes a squamosa-promoter binding protein (SBP) transcription factor, underlies this transformation[25]. It has been shown that a de novo mutation occurred during maize domestication, causing a single amino acid substitution (Lys to Asn) in the TGA1 protein, altering its binding activity to its target genes, including a group of MADS-box genes that regulate glume identity[26].

    Prolificacy, the number of ears per plants, is also a domestication trait. It has been shown that grassy tillers 1 (gt1), which encodes an HD-ZIP I transcription factor, suppresses prolificacy by promoting lateral bud dormancy and suppressing elongation of the later ear branches[27]. The expression of gt1 is induced by shading and requires the activity of tb1, suggesting that gt1 acts downstream of tb1 to mediate the suppressed branching activity in response to shade. Later studies mapped a large effect QTL for prolificacy (prol1.1) to a 2.7 kb 'causative region' upstream of the gt1gene[28]. In addition, a recent study identified a new QTL, qEN7 (for ear number on chromosome 7). Zm00001d020683, which encodes a putative INDETERMINATE DOMAIN (IDD) transcription factor, was identified as the likely candidate gene based on its expression pattern and signature of selection during maize improvement[29]. However, its functionality and regulatory relationship with tb1 and gt1 remain to be elucidated.

    Smaller leaf angle and thus more compact plant architecture is a desired trait for modern maize varieties. Tian et al.[30] used a maize-teosinte BC2S3 population and cloned two QTLs (Upright Plant Architecture1 and 2 [UPA1 and UPA2]) that regulate leaf angle. Interestingly, the authors showed that the functional variant of UPA2 is a 2-bp InDel located 9.5 kb upstream of ZmRAVL1, which encodes a B3 domain transcription factor. The 2-bp Indel flanks the binding site of the transcription factor Drooping Leaf1 (DRL1)[31], which represses ZmRAVL1 expression through interacting with Liguleless1 (LG1), a SBP-box transcription factor essential for leaf ligule and auricle development[32]. UPA1 encodes brassinosteroid C-6 oxidase1 (brd1), a key enzyme for biosynthesis of active brassinolide (BR). The teosinte-derived allele of UPA2 binds DRL1 more strongly, leading to lower expression of ZmRAVL1 and thus, lower expression of brd1 and BR levels, and ultimately smaller leaf angle. Notably, the authors demonstrated that the teosinte-derived allele of UPA2 confers enhanced yields under high planting densities when introgressed into modern maize varieties[30, 33].

    Maize plants exhibit salient vegetative phase change, which marks the vegetative transition from the juvenile stage to the adult stage, characterized by several changes in maize leaves produced before and after the transition, such as production of leaf epicuticular wax and epidermal hairs. Previous studies reported that Glossy15 (Gl15), which encodes an AP2-like transcription factor, promotes juvenile leaf identity and suppressing adult leaf identity. Ectopic overexpression of Gl15 causes delayed vegetative phase change and flowering, while loss-of-function gl15 mutant displayed earlier vegetative phase change[34]. In another study, Gl15 was identified as a major QTL (qVT9-1) controlling the difference in the vegetative transition between maize and teosinte. Further, it was shown that a pre-existing low-frequency standing variation, SNP2154-G, was selected during domestication and likely represents the causal variation underlying differential expression of Gl15, and thus the difference in the vegetative transition between maize and teosinte[35].

    A number of studies documented evidence that tassels replace upper ears1 (tru1) is a key regulator of the conversion of the male terminal lateral inflorescence (tassel) in teosinte to a female terminal inflorescence (ear) in maize. tru1 encodes a BTB/POZ ankyrin repeat domain protein, and it is directly targeted by tb1, suggesting their close regulatory relationship[36]. In addition, a number of regulators of maize inflorescence morphology, were also shown as selective targets during maize domestication, including ramosa1 (ra1)[37, 38], which encodes a putative transcription factor repressing inflorescence (the ear and tassel) branching, Zea Agamous-like1 (zagl1)[39], which encodes a MADS-box transcription factor regulating flowering time and ear size, Zea floricaula leafy2 (zfl2, homologue of Arabidopsis Leafy)[40, 41], which likely regulates ear rank number, and barren inflorescence2 (bif2, ortholog of the Arabidopsis serine/threonine kinase PINOID)[42, 43], which regulates the formation of spikelet pair meristems and branch meristems on the tassel. The detailed regulatory networks of these key regulators of maize inflorescence still remain to be further elucidated.

    Kernel row number (KRN) and kernel weight are two important determinants of maize yield. A number of domestication genes modulating KRN and kernel weight have been identified and cloned, including KRN1, KRN2, KRN4 and qHKW1. KRN4 was mapped to a 3-kb regulatory region located ~60 kb downstream of Unbranched3 (UB3), which encodes a SBP transcription factor and negatively regulates KRN through imparting on multiple hormone signaling pathways (cytokinin, auxin and CLV-WUS)[44, 45]. Studies have also shown that a harbinger TE in the intergenic region and a SNP (S35) in the third exon of UB3 act in an additive fashion to regulate the expression level of UB3 and thus KRN[46].

    KRN1 encodes an AP2 transcription factor that pleiotropically affects plant height, spike density and grain size of maize[47], and is allelic to ids1/Ts6 (indeterminate spikelet 1/Tassel seed 6)[48]. Noteworthy, KRN1 is homologous to the wheat domestication gene Q, a major regulator of spike/spikelet morphology and grain threshability in wheat[49].

    KRN2 encodes a WD40 domain protein and it negatively regulates kernel row number[50]. Selection in a ~700-bp upstream region (containing the 5’UTR) of KRN2 during domestication resulted in reduced expression and thus increased kernel row number. Interestingly, its orthologous gene in rice, OsKRN2, was shown also a selected gene during rice domestication to negatively regulate secondary panicle branches and thus grain number. These observations suggest convergent selection of yield-related genes occurred during parallel domestication of cereal crops.

    qHKW1 is a major QTL for hundred-kernel weight (HKW)[51]. It encodes a CLAVATA1 (CLV1)/BARELY ANY MERISTEM (BAM)-related receptor kinase-like protein positively regulating HKW. A 8.9 Kb insertion in its promoter region was find to enhance its expression, leading to enhanced HKW[52]. In addition, Chen et al.[53] reported cloning of a major QTL for kernel morphology, qKM4.08, which encodes ZmVPS29, a retromer complex component. Sequencing and association analysis revealed that ZmVPS29 was a selective target during maize domestication. They authors also identified two significant polymorphic sites in its promoter region significantly associated with the kernel morphology. Moreover, a strong selective signature was detected in ZmSWEET4c during maize domestication. ZmSWEET4c encodes a hexose transporter protein functioning in sugar transport across the basal endosperm transfer cell layer (BETL) during seed filling[54]. The favorable alleles of these genes could serve as valuable targets for genetic improvement of maize yield.

    In a recent effort to more systematically analyze teosinte alleles that could contribute to yield potential of maize, Wang et al.[55] constructed four backcrossed maize-teosinte recombinant inbred line (RIL) populations and conducted detailed phenotyping of 26 agronomic traits under five environmental conditions. They identified 71 QTL associated with 24 plant architecture and yield related traits through inclusive composite interval mapping. Interestingly, they identified Zm00001eb352570 and Zm00001eb352580, both encode ethylene-responsive transcription factors, as two key candidate genes regulating ear height and the ratio of ear to plant height. Chen et al.[56] constructed a teosinte nested association mapping (TeoNAM) population, and performed joint-linkage mapping and GWAS analyses of 22 domestication and agronomic traits. They identified the maize homologue of PROSTRATE GROWTH1, a rice domestication gene controlling the switch from prostrate to erect growth, is also a QTL associated with tillering in teosinte and maize. Additionally, they also detected multiple QTL for days-to-anthesis (such as ZCN8 and ZmMADS69) and other traits (such as tassel branch number and tillering) that could be exploited for maize improvement. These lines of work highlight again the value of mining the vast amounts of superior alleles hidden in teosinte for future maize genetic improvement.

    Loss of seed shattering was also a key trait of maize domestication, like in other cereals. shattering1 (sh1), which encodes a zinc finger and YABBY domain protein regulating seed shattering. Interesting, sh1 was demonstrated to undergo parallel domestication in several cereals, including rice, maize, sorghum, and foxtail millet[57]. Later studies showed that the foxtail millet sh1 gene represses lignin biosynthesis in the abscission layer, and that an 855-bp Harbinger transposable element insertion in sh1 causes loss of seed shattering in foxtail millet[58].

    In addition to morphological traits, a number of physiological and nutritional related traits have also been selected during maize domestication. Based on survey of the nucleotide diversity, Whitt et al.[59] reported that six genes involved in starch metabolism (ae1, bt2, sh1, sh2, su1 and wx1) are selective targets during maize domestication. Palaisa et al.[60] reported selection of the Y1 gene (encoding a phytoene synthase) for increased nutritional value. Karn et al.[61] identified two, three, and six QTLs for starch, protein and oil respectively and showed that teosinte alleles can be exploited for the improvement of kernel composition traits in modern maize germplasm. Fan et at.[62] reported a strong selection imposed on waxy (wx) in the Chinese waxy maize population. Moreover, a recent exciting study reported the identification of a teosinte-derived allele of teosinte high protein 9 (Thp9) conferring increased protein level and nitrogen utilization efficiency (NUE). It was further shown that Thp9 encodes an asparagine synthetase 4 and that incorrect splicing of Thp9-B73 transcripts in temperate maize varieties is responsible for its diminished expression, and thus reduced NUE and protein content[63].

    Teosintes is known to confer superior disease resistance and adaptation to extreme environments (such as low phosphorus and high salinity). de Lange et al. and Lennon et al.[6466] reported the identification of teosinte-derived QTLs for resistance to gray leaf spot and southern leaf blight in maize. Mano & Omori reported that teosinte-derived QTLs could confer flooding tolerance[67]. Feng et al.[68] identified four teosinte-derived QTL that could improve resistance to Fusarium ear rot (FER) caused by Fusarium verticillioides. Recently, Wang et al.[69] reported a MYB transcription repressor of teosinte origin (ZmMM1) that confers resistance to northern leaf blight (NLB), southern corn rust (SCR) and gray leaf spot (GLS) in maize, while Zhang et al.[70] reported the identification of an elite allele of SNP947-G ZmHKT1 (encoding a sodium transporter) derived from teosinte can effectively improve salt tolerance via exporting Na+ from the above-ground plant parts. Gao et al.[71] reported that ZmSRO1d-R can regulate the balance between crop yield and drought resistance by increasing the guard cells' ROS level, and it underwent selection during maize domestication and breeding. These studies argue for the need of putting more efforts to tapping into the genetic resources hidden in the maize’s wild relatives. The so far cloned genes involved in maize domestication are summarized in Table 1. Notably, the enrichment of transcription factors in the cloned domestication genes highlights a crucial role of transcriptional re-wiring in maize domestication.

    Table 1.  Key domestication genes cloned in maize.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    tb1Plant architectureTCP transcription factorIncreased expression~60 kb upstream of tb1 enhancing expression[1822]
    tga1Hardened fruitcaseSBP-domain transcription factorProtein functionA SNP in exon (K-N)[25, 26]
    gt1Plant architectureHomeodomain leucine zipperIncreased expressionprol1.1 in 2.7 kb upstream of the promoter region increasing expression[27, 28]
    Zm00001d020683Plant architectureINDETERMINATE DOMAIN transcription factorProtein functionUnknown[29]
    UPA1Leaf angleBrassinosteroid C-6 oxidase1Protein functionUnknown[30]
    UPA2Leaf angleB3 domain transcription factorIncreased expressionA 2 bp indel in 9.5 kb upstream of ZmRALV1[30]
    Gl15Vegetative phase changeAP2-like transcription factorAltered expressionSNP2154: a stop codon (G-A)[34, 35]
    tru1Plant architectureBTB/POZ ankyrin repeat proteinIncreased expressionUnknown[36]
    ra1Inflorescence architectureTranscription factorAltered expressionUnknown[37, 38]
    zflPlant architectureTranscription factorAltered expressionUnknown[40, 41]
    UB3Kernel row numberSBP-box transcription factorAltered expressionA TE in the intergenic region;[4446]
    SNP (S35): third exon of UB3
    (A-G) increasing expression of UB3 and KRN
    KRN1/ids1/Ts6Kernel row numberAP2 Transcription factorIncreased expressionUnknown[47, 48]
    KRN2Kernel row numberWD40 domainDecreased expressionUnknown[50]
    qHKW1Kernel row weightCLV1/BAM-related receptor kinase-like proteinIncreased expression8.9 kb insertion upstream of HKW[51, 52]
    ZmVPS29Kernel morphologyA retromer complex componentProtein functionTwo SNPs (S-1830 and S-1558) in the promoter of ZmVPS29[53]
    ZmSWEET4cSeed fillingHexose transporterProtein functionUnknown[54]
    ZmSh1ShatteringA zinc finger and YABBY transcription factorProtein functionUnknown[57, 58]
    Thp9Nutrition qualityAsparagine synthetase 4 enzymeProtein functionA deletion in 10th intron of Thp9 reducing NUE and protein content[63]
    ZmMM1Biotic stressMYB Transcription repressorProtein functionUnknown[69]
    ZmHKT1Abiotic stressA sodium transporterProtein functionSNP947-G: a nonsynonymous variation increasing salt tolerance[70]
    ZmSRO1d-RDrought resistance and productionPolyADP-ribose polymerase and C-terminal RST domainProtein functionThree non-synonymous variants: SNP131 (A44G), SNP134 (V45A) and InDel433[71]
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    After its domestication from its wild progenitor teosinte in southwestern Mexico in the tropics, maize has now become the mostly cultivated crop worldwide owing to its extensive range expansion and adaptation to diverse environmental conditions (such as temperature and day length). A key prerequisite for the spread of maize from tropical to temperate regions is reduced photoperiod sensitivity[72]. It was recently shown that CENTRORADIALIS 8 (ZCN8), an Flowering Locus T (FT) homologue, underlies a major quantitative trait locus (qDTA8) for flowering time[73]. Interestingly, it has been shown that step-wise cis-regulatory changes occurred in ZCN8 during maize domestication and post-domestication expansion. SNP-1245 is a target of selection during early maize domestication for latitudinal adaptation, and after its fixation, selection of InDel-2339 (most likely introgressed from Zea mays ssp. Mexicana) likely contributed to the spread of maize from tropical to temperate regions[74].

    ZCN8 interacts with the basic leucine zipper transcription factor DLF1 (Delayed flowering 1) to form the florigen activation complex (FAC) in maize. Interestingly, DFL1 was found to underlie qLB7-1, a flowering time QTL identified in a BC2S3 population of maize-teosinte. Moreover, it was shown that DLF1 directly activates ZmMADS4 and ZmMADS67 in the shoot apex to promote floral transition[75]. In addition, ZmMADS69 underlies the flowering time QTL qDTA3-2 and encodes a MADS-box transcription factor. It acts to inhibit the expression of ZmRap2.7, thereby relieving its repression on ZCN8 expression and causing earlier flowering. Population genetic analyses showed that DLF1, ZmMADS67 and ZmMADS69 are all targets of artificial selection and likely contributed to the spread of maize from the tropics to temperate zones[75, 76].

    In addition, a few genes regulating the photoperiod pathway and contributing to the acclimation of maize to higher latitudes in North America have been cloned, including Vgt1, ZmCCT (also named ZmCCT10), ZmCCT9 and ZmELF3.1. Vgt1 was shown to act as a cis-regulatory element of ZmRap2.7, and a MITE TE located ~70 kb upstream of Vgt1 was found to be significantly associated with flowering time and was a major target for selection during the expansion of maize to the temperate and high-latitude regions[7779]. ZmCCT is another major flowering-time QTL and it encodes a CCT-domain protein homologous to rice Ghd7[80]. Its causal variation is a 5122-bp CACTA-like TE inserted ~2.5 kb upstream of ZmCCT10[72, 81]. ZmCCT9 was identified a QTL for days to anthesis (qDTA9). A Harbinger-like TE located ~57 kb upstream of ZmCCT9 showed the most significant association with DTA and thus believed to be the causal variation[82]. Notably, the CATCA-like TE of ZmCCT10 and the Harbinger-like TE of ZmCCT9 are not observed in surveyed teosinte accessions, hinting that they are de novo mutations occurred after the initial domestication of maize[72, 82]. ZmELF3.1 was shown to underlie the flowering time QTL qFT3_218. It was demonstrated that ZmELF3.1 and its homolog ZmELF3.2 can form the maize Evening Complex (EC) through physically interacting with ZmELF4.1/ZmELF4.2, and ZmLUX1/ZmLUX2. Knockout mutants of Zmelf3.1 and Zmelf3.1/3.2 double mutant presented delayed flowering under both long-day and short-day conditions. It was further shown that the maize EC promote flowering through repressing the expression of several known flowering suppressor genes (e.g., ZmCCT9, ZmCCT10, ZmCOL3, ZmPRR37a and ZmPRR73), and consequently alleviating their inhibition on several maize florigen genes (ZCN8, ZCN7 and ZCN12). Insertion of two closely linked retrotransposon elements upstream of the ZmELF3.1 coding region increases the expression of ZmELF3.1, thus promoting flowering[83]. The increase frequencies of the causal TEs in Vgt1, ZmCCT10, ZmCCT9 and ZmELF3.1 in temperate maize compared to tropical maize highlight a critical role of these genes during the spread and adaptation of maize to higher latitudinal temperate regions through promoting flowering under long-day conditions[72,8183].

    In addition, Barnes et al.[84] recently showed that the High Phosphatidyl Choline 1 (HPC1) gene, which encodes a phospholipase A1 enzyme, contributed to the spread of the initially domesticated maize from the warm Mexican southwest to the highlands of Mexico and South America by modulating phosphatidylcholine levels. The Mexicana-derived allele harbors a polymorphism and impaired protein function, leading to accelerated flowering and better fitness in highlands.

    Besides the above characterized QTLs and genes, additional genetic elements likely also contributed to the pre-Columbia spreading of maize. Hufford et al.[85] proposed that incorporation of mexicana alleles into maize may helped the expansion of maize to the highlands of central Mexico based on detection of bi-directional gene flow between maize and Mexicana. This proposal was supported by a recent study showing evidence of introgression for over 10% of the maize genome from the mexicana genome[86]. Consistently, Calfee et al.[87] found that sequences of mexicana ancestry increases in high-elevation maize populations, supporting the notion that introgression from mexicana facilitating adaptation of maize to the highland environment. Moreover, a recent study examined the genome-wide genetic diversity of the Zea genus and showed that dozens of flowering-related genes (such as GI, BAS1 and PRR7) are associated with high-latitude adaptation[88]. These studies together demonstrate unequivocally that introgression of genes from Mexicana and selection of genes in the photoperiod pathway contributed to the spread of maize to the temperate regions.

    The so far cloned genes involved in pre-Columbia spread of maize are summarized in Fig. 2 and Table 2.

    Figure 2.  Genes involved in Pre-Columbia spread of maize to higher latitudes and the temperate regions. The production of world maize in 2020 is presented by the green bar in the map from Ritchie et al. (2023). Ritchie H, Rosado P, and Roser M. 2023. "Agricultural Production". Published online at OurWorldInData.org. Retrieved from: 'https:ourowrldindata.org/agricultural-production' [online Resource].
    Table 2.  Flowering time related genes contributing to Pre-Columbia spread of maize.
    GeneFunctional annotationCausative changeReferences
    ZCN8Florigen proteinSNP-1245 and Indel-2339 in promoter[73, 74]
    DLF1Basic leucine zipper transcription factorUnknown[75]
    ZmMADS69MADS-box transcription factorUnknown[76]
    ZmRap2.7AP2-like transcription factorMITE TE inserted ~70 kb upstream[7779]
    ZmCCTCCT-domain protein5122-bp CACTA-like TE inserted ~2.5 kb upstream[72,81]
    ZmCCT9CCT transcription factorA harbinger-like element at 57 kb upstream[82]
    ZmELF3.1Unknownwo retrotransposons in the promote[84]
    HPC1Phospholipase A1 enzymUnknown[83]
    ZmPRR7UnknownUnknown[88]
    ZmCOL9CO-like-transcription factorUnknown[88]
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    Subsequent to domestication ~9,000 years ago, maize has been continuously subject to human selection during the post-domestication breeding process. Through re-sequencing analysis of 35 improved maize lines, 23 traditional landraces and 17 wild relatives, Hufford et al.[15] identified 484 and 695 selective sweeps during maize domestication and improvement, respectively. Moreover, they found that about a quarter (23%) of domestication sweeps (107) were also selected during improvement, indicating that a substantial portion of the domestication loci underwent continuous selection during post-domestication breeding.

    Genetic improvement of maize culminated in the development of high planting density tolerant hybrid maize to increase grain yield per unit land area[89, 90]. To investigate the key morphological traits that have been selected during modern maize breeding, we recently conducted sequencing and phenotypic analyses of 350 elite maize inbred lines widely used in the US and China over the past few decades. We identified four convergently improved morphological traits related to adapting to increased planting density, i.e., reduced leaf angle, reduced tassel branch number (TBN), reduced relative plant height (EH/PH) and accelerated flowering. Genome-wide Association Study (GWAS) identified a total of 166 loci associated with the four selected traits, and found evidence of convergent increases in allele frequency at putatively favorable alleles for the identified loci. Moreover, genome scan using the cross-population composite likelihood ratio approach (XP-CLR) identified a total of 1,888 selective sweeps during modern maize breeding in the US and China. Gene ontology analysis of the 5,356 genes encompassed in the selective sweeps revealed enrichment of genes related to biosynthesis or signaling processes of auxin and other phytohormones, and in responses to light, biotic and abiotic stresses. This study provides a valuable resource for mining genes regulating morphological and physiological traits underlying adaptation to high-density planting[91].

    In another study, Li et al.[92] identified ZmPGP1 (ABCB1 or Br2) as a selected target gene during maize domestication and genetic improvement. ZmPGP1 is involved in auxin polar transport, and has been shown to have a pleiotropic effect on plant height, stalk diameter, leaf length, leaf angle, root development and yield. Sequence and phenotypic analyses of ZmPGP1 identified SNP1473 as the most significant variant for kernel length and ear grain weight and that the SNP1473T allele is selected during both the domestication and improvement processes. Moreover, the authors identified a rare allele of ZmPGP1 carrying a 241-bp deletion in the last exon, which results in significantly reduced plant height and ear height and increased stalk diameter and erected leaves, yet no negative effect on yield[93], highlighting a potential utility in breeding high-density tolerant maize cultivars.

    Shade avoidance syndrome (SAS) is a set of adaptive responses triggered when plants sense a reduction in the red to far-red light (R:FR) ratio under high planting density conditions, commonly manifested by increased plant height (and thus more prone to lodging), suppressed branching, accelerated flowering and reduced resistance to pathogens and pests[94, 95]. High-density planting could also cause extended anthesis-silking interval (ASI), reduced tassel size and smaller ear, and even barrenness[96, 97]. Thus, breeding of maize cultivars of attenuated SAS is a priority for adaptation to increased planting density.

    Extensive studies have been performed in Arabidopsis to dissect the regulatory mechanism of SAS and this topic has been recently extensively reviewed[98]. We recently showed that a major signaling mechanism regulating SAS in Arabidopsis is the phytochrome-PIFs module regulates the miR156-SPL module-mediated aging pathway[99]. We proposed that in maize there might be a similar phytochrome-PIFs-miR156-SPL regulatory pathway regulating SAS and that the maize SPL genes could be exploited as valuable targets for genetic improvement of plant architecture tailored for high-density planting[100].

    In support of this, it has been shown that the ZmphyBs (ZmphyB1 and ZmphyB2), ZmphyCs (ZmphyC1 and ZmphyC2) and ZmPIFs are involved in regulating SAS in maize[101103]. In addition, earlier studies have shown that as direct targets of miR156s, three homologous SPL transcription factors, UB2, UB3 and TSH4, regulate multiple agronomic traits including vegetative tillering, plant height, tassel branch number and kernel row number[44, 104]. Moreover, it has been shown that ZmphyBs[101, 105] and ZmPIF3.1[91], ZmPIF4.1[102] and TSH4[91] are selective targets during modern maize breeding (Table 3).

    Table 3.  Selective genes underpinning genetic improvement during modern maize breeding.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    ZmPIF3.1Plant heightBasic helix-loop-helix transcription factorIncreased expressionUnknown[91]
    TSH4Tassel branch numberTranscription factorAltered expressionUnknown[91]
    ZmPGP1Plant architectureATP binding cassette transporterAltered expressionA 241 bp deletion in the last exon of ZmPGP1[92, 93]
    PhyB2Light signalPhytochrome BAltered expressionA 10 bp deletion in the translation start site[101]
    ZmPIF4.1Light signalBasic helix-loop-helix transcription factorAltered expressionUnknown[102]
    ZmKOB1Grain yieldGlycotransferase-like proteinProtein functionUnknown[121]
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    In a recent study to dissect the signaling process regulating inflorescence development in response to the shade signal, Kong et al.[106] compared the gene expression changes along the male and female inflorescence development under simulated shade treatments and normal light conditions, and identified a large set of genes that are co-regulated by developmental progression and simulated shade treatments. They found that these co-regulated genes are enriched in plant hormone signaling pathways and transcription factors. By network analyses, they found that UB2, UB3 and TSH4 act as a central regulatory node controlling maize inflorescence development in response to shade signal, and their loss-of-function mutants exhibit reduced sensitivity to simulated shade treatments. This study provides a valuable genetic source for mining and manipulating key shading-responsive genes for improved tassel and ear traits under high density planting conditions.

    Nowadays, global maize production is mostly provided by hybrid maize, which exhibits heterosis (or hybrid vigor) in yields and stress tolerance over open-pollinated varieties[3]. Hybrid maize breeding has gone through several stages, from the 'inbred-hybrid method' stage by Shull[107] and East[108] in the early twentieth century, to the 'double-cross hybrids' stage (1930s−1950s) by Jones[109], and then the 'single-cross hybrids' stage since the 1960s. Since its development, single-cross hybrid was quickly adopted globally due to its superior heterosis and easiness of production[3].

    Single-cross maize hybrids are produced from crossing two unrelated parental inbred lines (female × male) belonging to genetically distinct pools of germplasm, called heterotic groups. Heterotic groups allow better exploitation of heterosis, since inter-group hybrids display a higher level of heterosis than intra-group hybrids. A specific pair of female and male heterotic groups expressing pronounced heterosis is termed as a heterotic pattern[110, 111]. Initially, the parental lines were derived from a limited number of key founder inbred lines and empirically classified into different heterotic groups (such as SSS and NSS)[112]. Over time, they have expanded dramatically, accompanied by formation of new 'heterotic groups' (such as Iodent, PA and PB). Nowadays, Stiff Stalk Synthetics (SSS) and PA are generally used as FHGs (female heterotic groups), while Non Stiff Stalk (NSS), PB and Sipingtou (SPT) are generally used as the MHGs (male heterotic groups) in temperate hybrid maize breeding[113].

    With the development of molecular biology, various molecular markers, ranging from RFLPs, SSRs, and more recently high-density genome-wide SNP data have been utilized to assign newly developed inbred lines into various heterotic groups, and to guide crosses between heterotic pools to produce the most productive hybrids[114116]. Multiple studies with molecular markers have suggested that heterotic groups have diverged genetically over time for better heterosis[117120]. However, there has been a lack of a systematic assessment of the effect and contribution of breeding selection on phenotypic improvement and the underlying genomic changes of FHGs and MHGs for different heterotic patterns on a population scale during modern hybrid maize breeding.

    To systematically assess the phenotypic improvement and the underlying genomic changes of FHGs and MHGs during modern hybrid maize breeding, we recently conducted re-sequencing and phenotypic analyses of 21 agronomic traits for a panel of 1,604 modern elite maize lines[121]. Several interesting observations were made: (1) The MHGs experienced more intensive selection than the FMGs during the progression from era I (before the year 2000) to era II (after the year 2000). Significant changes were observed for 18 out of 21 traits in the MHGs, but only 10 of the 21 traits showed significant changes in the FHGs; (2) The MHGs and FHGs experienced both convergent and divergent selection towards different sets of agronomic traits. Both the MHGs and FHGs experienced a decrease in flowering time and an increase in yield and plant architecture related traits, but three traits potentially related to seed dehydration rate were selected in opposite direction in the MHGs and FHGs. GWAS analysis identified 4,329 genes associated with the 21 traits. Consistent with the observed convergent and divergent changes of different traits, we observed convergent increase for the frequencies of favorable alleles for the convergently selected traits in both the MHGs and FHGs, and anti-directional changes for the frequencies of favorable alleles for the oppositely selected traits. These observations highlight a critical contribution of accumulation of favorable alleles to agronomic trait improvement of the parental lines of both FHGs and MHGs during modern maize breeding.

    Moreover, FST statistics showed increased genetic differentiation between the respective MHGs and FHGs of the US_SS × US_NSS and PA × SPT heterotic patterns from era I to era II. Further, we detected significant positive correlations between the number of accumulated heterozygous superior alleles of the differentiated genes with increased grain yield per plant and better parent heterosis, supporting a role of the differentiated genes in promoting maize heterosis. Further, mutational and overexpressional studies demonstrated a role of ZmKOB1, which encodes a putative glycotransferase, in promoting grain yield[121]. While this study complemented earlier studies on maize domestication and variation maps in maize, a pitfall of this study is that variation is limited to SNP polymorphisms. Further exploitation of more variants (Indels, PAVs, CNVs etc.) in the historical maize panel will greatly deepen our understanding of the impact of artificial selection on the maize genome, and identify valuable new targets for genetic improvement of maize.

    The ever-increasing worldwide population and anticipated climate deterioration pose a great challenge to global food security and call for more effective and precise breeding methods for crops. To accommodate the projected population increase in the next 30 years, it is estimated that cereal production needs to increase at least 70% by 2050 (FAO). As a staple cereal crop, breeding of maize cultivars that are not only high-yielding and with superior quality, but also resilient to environmental stresses, is essential to meet this demand. The recent advances in genome sequencing, genotyping and phenotyping technologies, generation of multi-omics data (including genomic, phenomic, epigenomic, transcriptomic, proteomic, and metabolomic data), creation of novel superior alleles by genome editing, development of more efficient double haploid technologies, integrating with machine learning and artificial intelligence are ushering the transition of maize breeding from the Breeding 3.0 stage (biological breeding) into the Breeding 4.0 stage (intelligent breeding)[122, 123]. However, several major challenges remain to be effectively tackled before such a transition could be implemented. First, most agronomic traits of maize are controlled by numerous small-effect QTL and complex genotype-environment interactions (G × E). Thus, elucidating the contribution of the abundant genetic variation in the maize population to phenotypic plasticity remains a major challenge in the post-genomic era of maize genetics and breeding. Secondly, most maize cultivars cultivated nowadays are hybrids that exhibit superior heterosis than their parental lines. Hybrid maize breeding involves the development of elite inbred lines with high general combining ability (GCA) and specific combining ability (SCA) that allows maximal exploitation of heterosis. Despite much effort to dissect the mechanisms of maize heterosis, the molecular basis of maize heterosis is still a debated topic[124126]. Thirdly, only limited maize germplasm is amenable to genetic manipulation (genetic transformation, genome editing etc.), which significantly hinders the efficiency of genetic improvement. Development of efficient genotype-independent transformation procedure will greatly boost maize functional genomic research and breeding. Noteworthy, the Smart Corn System recently launched by Bayer is promised to revolutionize global corn production in the coming years. At the heart of the new system is short stature hybrid corn (~30%−40% shorter than traditional hybrids), which offers several advantages: sturdier stems and exceptional lodging resistance under higher planting densities (grow 20%−30% more plants per hectare), higher and more stable yield production per unit land area, easier management and application of plant protection products, better use of solar energy, water and other natural resources, and improved greenhouse gas footprint[127]. Indeed, a new age of maize green revolution is yet to come!

    This work was supported by grants from the Key Research and Development Program of Guangdong Province (2022B0202060005), National Natural Science Foundation of China (32130077) and Hainan Yazhou Bay Seed Lab (B21HJ8101). We thank Professors Hai Wang (China Agricultural University) and Jinshun Zhong (South China Agricultural University) for valuable comments and helpful discussion on the manuscript. We apologize to authors whose excellent work could not be cited due to space limitations.

  • The authors declare that they have no conflict of interest. Haiyang Wang is an Editorial Board member of Seed Biology who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and his research groups.

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  • Cite this article

    Qian R, Ye Y, Ma X, Gao H, Hu Q, et al. 2025. Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers. Ornamental Plant Research 5: e001 doi: 10.48130/opr-0024-0031
    Qian R, Ye Y, Ma X, Gao H, Hu Q, et al. 2025. Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers. Ornamental Plant Research 5: e001 doi: 10.48130/opr-0024-0031

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Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers

Ornamental Plant Research  5 Article number: e001  (2025)  |  Cite this article

Abstract: Clematis lanuginosa, a valuable ornamental plant in Zhejiang Province, China, produces flowers that are blue–purple, a rare flower colour. In this study, to explore the anthocyanin synthesis mechanism involved in flower colour formation in C. lanuginosa, metabolome, and transcriptome sequencing was performed at six flower development stages. Metabolome analysis revealed 25 anthocyanin compounds and 22 differentially expressed metabolites. Cyanidin-3,5-O-diglucoside, cyanidin-3,5,3'-O-triglucoside, cyanidin-3-O-rutinoside-5-O-glucoside, delphinidin-3-O-glucoside, and petunidin-3-O-glucoside may promote the formation of blue–purple colour in flowers. The combined analysis results revealed that the transcript41913_f2p0_1152 gene (MYB-like) may be a key gene in C. lanuginosa blue–purple flower colour development. These results provide the basis for further research on the blue–purple flower colour of C. lanuginosa.

    • Clematis lanuginosa belongs to the Ranunculaceae family and is one of the most primitive and highly important parents of Clematis breeding[1]. It is also a unique wild resource in China and a key protected wild plant in Zhejiang Province and is distributed only in northeast of the province. The flowers of C. lanuginosa are usually blue–purple in colour[2,3].

      Most plant flower colour is due to the presence of pigment compounds in chloroplasts or vacuoles in flower tissue. Currently known plant pigments are classified into three main categories: carotenoids, alkaloids, and flavonoids[4]. Anthocyanins constitute a large class of flavonoid compounds widely found in plant roots, stems, leaves, flowers, and fruits[5]. More than 600 anthocyanin glycosides are found in nature[6]. The most common anthocyanins in plants are six widely distributed anthocyanin glycoside derivatives: delphinidin, malvidin, pelargonidin, cyanidin, peonidin, and petunidin[7]. The type and content of anthocyanin glycosides are the main determinants of flower colour. For example, pink to purple‒red is produced by cyanidin, orange‒red to red is produced by pelargonidin, and purple to blue colouration is produced by delphinidin and its derivative malvidin pigment[8]. In addition, changes in anthocyanin content affect the final presentation of flower colour. For example, a gradual increase in cyanidin content is associated with a colour change from light to dark red[9].

      Transcription factors regulate the expression intensities and patterns of structural genes associated with anthocyanin biosynthesis[10]. Wild-type petunias usually exhibit coloured stems, blue flowers, and dark brown seeds. When the coding sequence of the morning glory transcription factor InMYB1 is mutated, the stems, flowers, and seeds of the mutant plants become red, white, and white, respectively. A frameshift in the gene encoding another transcription factor, InWDR1, produces green stems, white flowers, and ivory seeds[11]. When the second intron of the TfMYB1 gene is inserted into an En/Spm-like transposon, the expression of structural genes involved in the anthocyanin synthesis pathway, such as F3H, CHS, UFGT, DFR, and ANS, is significantly downregulated, resulting in a mutant with a 'flecked' grey‒purple colour mixed with purple spots due to the significant decrease in anthocyanin content in the petals[12]. A study in peas revealed that transposon insertion into a bHLH regulatory gene inactivates this transcription factor, resulting in the formation of white flowers[13].

      In C. lanuginosa, there are significant changes in flower colour across different flower development periods, and the blue–purple colour typically fades rapidly, greatly shortening the viewing period. An important goal for ornamental plant breeders is to optimize flower colour. However, there have been no reports on the biosynthesis and regulation of flavonoids in C. lanuginosa. In this study, metabolome and transcriptome analyses were used to explore colour changes during flower development in C. lanuginosa. Subsequently, key metabolites and genes were identified. The results of this study provide a direction for optimizing flower colour for higher ornamental value.

    • Plant materials were obtained from C. lanuginosa plants with consistent flower colour planted in the Clematis germplasm resource garden of the Zhejiang Institute of Subtropical Crops (120°63'54" E, 27°99'88" N), Zhejiang, China. The flower colours at the six selected stages were analysed and described according to the Royal Horticultural Society Colour Chart (RHSCC). Under the same lighting conditions, the middle part of the flower was compared to the RHSCC card. The RHSCC value of a flower is indicated by the code closest to the colour on the RHSCC card, and the colour range and number to which it belongs were determined[14,15]. After flower colour determination, flowers at the six stages were collected three times for subsequent research.

    • The C. lanuginosa flower samples were processed as follows: first, the flowers were vacuum freeze-dried and then powdered in a ball mill at 30 Hz for 15 min; then, 50 mg of the powder was weighed, dissolved in 500 μL of extraction solution (50% methanol aqueous solution containing 0.1% hydrochloric acid), vortexed for 5 min, sonicated for 5 min, centrifuged at 12,000 r/min and 4 °C for 3 min, and pipetted twice after centrifugation. The supernatant was pooled, filtered through a 0.22 μm microporous membrane, and stored in an injection vial for subsequent LC‒MS/MS analysis[16,17].

      MultiQuant 3.0.3 software was used to process the raw data acquired by tandem mass spectrometry (MS/MS) and ultra-performance liquid chromatography (UPLC) (ExionLC™ AD), including reference standard retention time and peak shape information. The metabolites in flowers of different colours were analysed by integrating the mass spectral peaks of the analytes in different samples to ensure the accuracy of the qualitative quantification, followed by cluster analysis and principal component analysis[18].

      The differentially abundant metabolites were identified based on the difference multiplier value (fold_change) and the p value obtained using the Wilcoxon rank sum test method[19] or t-test (or based on the difference factor value (fold_change) alone when there were no biological replicates). Metabolites with fold changes ≥ 2 and ≤ 0.5 were selected as the final differentially abundant metabolites. A clustering correlation heatmap with signs was generated using OmicStudio tools (www.omicstudio.cn).

    • Eighteen RNA samples (three biological replicates of each sample) were mixed in equal amounts. Single-molecule real-time (SMRT) library construction was performed as follows: oligo (dT)-enriched mRNA containing poly-A was generated and then reverse transcribed to cDNA using a SMARTer PCR cDNA Synthesis Kit and enriched cDNA was amplified by PCR. A sample of the cDNA was screened by BluePippin, and fragments larger than 4 kb were then enriched. Screened fragments were amplified by large-scale PCR to obtain sufficient cDNA and then subjected to damage repair, end repair, and ligation of the SMRT dumbbell-type linker. Nonscreened fragments were then mixed at an equimolar ratio with fragments larger than 4 kb to construct the final library.

      Exonuclease digestion was performed, and the unligated junctions at both ends of the cDNA sequence were then removed. The primers and DNA-binding polymerase were combined to generate a complete SMRTbell library. After the library was qualitatively analysed, the PacBio Sequel platform was used for sequencing according to the effective concentration of the library and the data output requirements. The official SMRT Link v6.0 software package was used to filter and process the data, and circular consensus sequence (CCS) data containing full-length and non-full-length fragments were generated. The full-length nonchimeric (FLNC) sequence and nonfull-length (nFL) nonchimeric sequence were subsequently used to determine whether the CCS contained the 5'-primer, 3'-primer, and poly-A sequences. The isoform-level clustering (ICE) algorithm was used to cluster the FLNC sequences of the same transcript to obtain the consensus sequence. The nFL nonchimeric sequences were subsequently used to correct the obtained consensus sequence, and the polished consensus sequence was ultimately obtained. The NGS data were used to correct the polished consensus sequence using LoREDC software.

    • Total RNA from eighteen C. lanuginosa flower samples (three biological replicates of each sample) was used to construct an mRNA library and high-throughput sequencing was performed using an Illumina HiSeq™ 2500 sequencer. Paired-end 150 bp data were obtained and de novo processed into transcripts and unigenes as reference sequences for subsequent analysis[20].

      The assembled unigenes were annotated using six databases (NR, SwissProt, Pfam, COG, GO, and KEGG). The BLAST2GO program performs GO classification and then maps unigenes to the KEGG database to determine their associated metabolic pathways. The quantitative expression results were then used for differential gene analysis between groups to obtain the differentially expressed genes; the difference analysis software used was DESeq2, with a screening threshold of |log2FC| > 1 and adjusted p < 0.05.

    • Real-time PCR primers were designed using Oligo 7 software. GAPDH was used as the internal reference gene. The primers used are shown in Table 1. The reaction system used was described in a previous study by Ye et al.[21]. Three technical replicates were performed to ensure the accuracy of the experiment, and the relative expression was calculated using the 2−ΔΔCᴛ method.

      Table 1.  Primers for qRT-PCR.

      Gene Primer-F Primer-R
      GAPDH AACCCCTGAGGAGATTCCA CACCACCCTTCAAGTGAGCAG
      ANS1 ATTGTGCACATCGGTGACAC CGACTCACTGACAAGTTCTG
      ANS2 TGCCTGGTCTCCAAGTGTAC CTAGCCCTCTATGCAGTATAC
      F3'H1 TCTTGTTGAGTACATCTTGG GACACTAGGTGGCAAGCGTG
      WD40 ATGAGCGAGAATTGCTGAGC TGCTACTGTGCATCCATCTG
      MYB1 AAGGCCGTTGGGATACGTTA ATCCTAGACCACCTGTTGCC
      MYB2 CACTGTTACCTCCGACGAGA CAGGTCTGTATCCTCGCTGT
      bHLH1 TGAAGACACCTGAAGGGCAA TCGTTGGAGCAAGATTCGGT
      bHLH2 TGCGAAGGAGTTCTGGTGAA ATGGCAAGAGAAGTCCCGAA
    • Combined analysis of the transcriptome and metabolomic data was performed using WGCNA with default parameters in R to simplify the gene expression data into coexpressed modules, normalize the FPKM values, and construct adjacency matrices. The phenotypic data were imported into the WGCNA package, and correlation-based associations between phenotypes and gene modules were identified using default settings. The WGCNA package was used to convert adjacency matrices into topological overlap matrices (TOMs)[22]. After network construction, transcripts with the same expression pattern were grouped to establish modules, and feature calculations were performed. Cytoscape 3.9.1 with default parameters was used to draw the network diagram[23].

    • The stages of development of C. lanuginosa flowers were analysed with a colorimetric card. The flower colours at the six stages are described as follows (Table 2): yellow‒green (bud stage, myfs1, N144D), light blue‒purple (colouring stage, myfs2, N88C), blue‒purple (early flowering stage, myfs3, N88B), bright blue‒purple (flowering stage, myfs4, 90D), lilac blue‒purple (after flowering stage, myfs5, 91B), and extremely lilac purple (end flowering stage, myfs6, 91C) (Table 2 & Fig. 1).

      Table 2.  Flower color identification of C. lanuginosa.

      Sequencing number Flower stage RSHCC Colour
      myfs1 Bud stage N144D yellow‒green
      myfs2 Coloration stage N88C light blue‒purple
      myfs3 Early flowering stage N88B dark purple
      myfs4 Flowering stage 90D bright blue‒purple
      myfs5 Post flowering stage 91B lilac purple stage
      myfs6 End flowering stage 91C extremely lilac purple stage

      Figure 1. 

      Different stages of C. lanuginosa flower development. (a) Bud stage (myfs1, yellow‒green, N144D); (b) Colouring stage (myfs2, light blue‒purple, light blue‒purple, N88C); (c) Early flowering stage (myfs3, dark purple, N88B); (d) Flowering stage (myfs4, bright blue‒purple, 90D); (e) After the flowering stage (myfs5, lilac purple stage, 91B); (f) End flowering stage (myfs6, extremely lilac purple stage, 91C).

    • Visual examination of the total ion current (TIC) plots revealed a strong instrumental analysis signal, a large peak capacity, and good retention time reproducibility for all the samples (Fig. 2a). The essential metabolite compositions in the different flowers of C. lanuginosa were determined by gas chromatography–mass spectrometry (GC‒MS), and this analysis identified 25 compounds in the flowers. Overall, the number of species and quantity of primary metabolites were greater than those of secondary metabolites, indicating that the flower samples presented vigorous primary metabolic activities. For these metabolites, principal component analysis (PCA) accurately grouped all the samples into distinct clusters, which reflected the obvious differences between the different flower stages (Fig. 2b).

      Figure 2. 

      Metabolomic analysis of the different flowers of C. lanuginosa. (a) Total ion current (TIC) plots of all the samples. (b) Principal component analysis (PCA) plots. Different groups are represented by different colours. (c) Heatmap of metabolites. Different groups are represented by different colours. The X-axis shows the different samples, and the Y-axis shows the metabolites in the different flowers of C. lanuginosa. The upregulated and downregulated genes are shown in red and blue, respectively.

      All the detected metabolite content data were normalized, and heatmaps were generated (Fig. 2c). The results revealed that cyanidin-3-O-galactoside and cyanidin-3-O-ruticoside-5-O-glucoside were expressed only in the myfs1 stage. The expression levels of cyanidin-3,5,3'-O-triglucosidecyanidin-3,5-O-diglucoside, cyanidin-3-O-ruticoside-5-O-glucoside, pelargonidin-3-O-rutin-5-O-glucoside, petunidin-3,5-O-diglucoside, petunidin-3-O-glucoside, and delphinidin-3-O-glucoside tended to increase during the first three developmental stages, peaked during the myfs3 period, and then tended to decrease.

      In addition, 22 differentially expressed metabolites were identified (Fig. 3a). The most differentially abundant metabolites were observed between myfs3 and myfs6. To further evaluate the specific expression profiles of the differentially expressed metabolites, we normalized the expression data of the differentially expressed metabolites were normalized and a heatmap generated (Fig. 3b). Petunidin 3,5-O-diglucoside, petunidin 3-O-glucoside, pelargonidin-3-O-ruticoside-5-O-glucoside, cyanidin-3,5,3'-O-triglucoside, cyanidin-3,5-O-diglucoside, and cyanidin-3-O-ruticoside-5-O-glucoside were expressed during the myfs1 period and then tended to increase, peaking in the early flowering period (myfs3), and then gradually decreasing.

      Figure 3. 

      Analysis of differentially expressed metabolites in flowers of C. lanuginosa. (a) Venn diagram of differentially expressed metabolites. Different comparison groups are represented by different colours. (b) Heatmap of differentially expressed metabolites. Different groups are represented by different colours. The X-axis shows the different samples, and the Y-axis shows the metabolites. The upregulated and downregulated genes are shown in red and blue, respectively.

    • A total of 773,680 CCS and 643,906 full-length nonchimeric (FLNC) sequence reads were obtained via full-length transcriptome sequencing. The FLNC cluster-corrected sequence was corrected using data from Illumina deep sequencing, and 54,793 gene sequences were obtained by removing redundant and similar sequences from the sequences obtained using CD-HIT software, with an average length of 2,365 bp and an N50 of 3,088. Gene function annotations in seven databases, namely, Nr, KEGG, Nt, Pfam, KOG/COG, SwissProt, and GO, revealed a total of 20,896 transcripts annotated; the most genes (17,656) were annotated in the SwissProt database, whereas the fewest genes were annotated in the GO and Pfam databases (13,377 each), and 7,416 genes were annotated in all seven databases (Fig. 4a). Using ANGEL software to predict the CDSs of the full-length transcriptome[24], 25,800 CDSs were obtained, of which 20.27% (5,230) had CDSs with lengths greater than 1,800 bp (Fig. 4b).

      Figure 4. 

      Transcript annotation and CDS prediction. (a) Venn diagram of the full-length transcriptome annotation results; different comparison groups are represented by different colours. (b) CDS prediction in the full-length transcriptome. The X-axis shows the CDS length, the left Y-axis shows the occurrence frequency of the CDSs of different lengths, and the right Y-axis shows the proportion of CDSs of different lengths. (c) DEGs during C. lanuginosa flower development. Red bars represent upregulated DEGs, and blue bars represent downregulated DEGs.

      To explore the molecular mechanisms involved in the development of C. lanuginosa flowers, different flower developmental stages of C. lanuginosa were analysed by next-generation transcriptome sequencing. From these sequences, 169.42 GB of clean data were obtained, with a filtering error rate of less than 0.025%, a Q30 between 94.6% and 95.17%, and a GC content between 45% and 45.77%. To explore the DEGs among C. lanuginosa flowers at the six different stages, the FPKM values of all genes in the samples were compared. The greatest number of DEGs was found between myfs1 and myfs6 (10,909; 6,216 upregulated and 4,693 downregulated), followed by myfs1 vs myfs5, and myfs2 vs myfs6. The smallest number of DEGs was found between myfs4 and myfs5, with a total of 3,382 DEGs (936 upregulated and 2,446 downregulated), followed by myfs3 vs myfs4, and myfs2 vs myfs3 (Fig. 5).

      Figure 5. 

      Identification of the expression profiles of eight unigenes in C. lanuginosa. The bar chart presents the relative expression levels of the genes, and the point line diagram presents the FPKM values of the unigenes. The left Y-axis represents the relative expression, whereas the right Y-axis represents the FPKM values of the unigenes. (a)−(e) Indicate statistical significance.

    • To further verify the reliability of the transcriptome sequencing results, eight unigenes (two ANS genes, two MYB genes, two bHLH genes, one F3'5'H gene, and one WD40 gene) were randomly selected for expression-level detection by qRT–PCR. As shown in Fig. 5, the expression trends of the qRT‒PCR results and the FPKM values from the sequencing results are consistent, suggesting that the transcriptome results and analysis are reliable and can be used for further analysis (Fig. 5).

    • For the WGCNA of the FPKM values, myfs1 was used as the control group, and 27 gene modules were identified according to the coexpression patterns of individual genes. These gene modules are represented in different colours in cluster maps and network heatmaps (Fig. 6a). Using different metabolites at different stages of C. lanuginosa development as phenotypic data, module‒trait correlations were analysed, and a sample tree map and a trait heatmap were constructed to clarify the expression of metabolite contents at different developmental stages (Fig. 6b).

      Figure 6. 

      Coexpression patterns of metabolism-related genes. (a) Cluster dendrogram and network heatmap of the genes calculated by the coexpression module. (b) Modular trait heatmap of the sample dendrograms at each developmental stage. (c) Hierarchical clustering showing the 11 modules with coexpressed genes. (d) Module‒trait associations based on Pearson correlation. The colours from green to red represent −1 to1.

      One of these 27 gene modules showed a very significant relationship with the differentially expressed metabolites. The MEroyalblue gene module is associated with anthocyanin biosynthesis, with the highest correlations with cyanidin-3,5-O-diglucoside, cyanidin-3-O-rhamnoside glycoside-5-O-glucoside, and cyanidin-3,5,3'-O-triglucoside, with R2 values of 0.69, 0.52, and 0.56, respectively (Fig. 6c & d).

      Correlation analysis of the differentially expressed MYB, bHLH, and WD40 transcription factors and differentially abundant metabolites in all the gene modules revealed that the MYB gene (transcript41913_f2p0_1152), cyanidin-3,5-O-diglucoside, cyanidin-3-O-rutin-5-O-glucoside, and cyanidin-3,5,3'-O-tri glucoside had the highest correlations, suggesting that this MYB gene may be a key gene involved in colour formation in C. lanuginosa (Fig. 7).

      Figure 7. 

      Correlation analysis of transcription factors and differentially expressed metabolites. (a) Heatmap of transcription factors and differentially expressed metabolites; the X-axis represents transcription factors, and the Y-axis represents differentially expressed metabolites. (b) Network plots of transcription factors and differentially expressed metabolites. Red represents transcription factors, and green circles represent differentially expressed metabolites.

    • Flower characteristics are important traits of many ornamental plants[25]; this is also true for C. lanuginosa (widely used in home gardening and urban greening). In C. lanuginosa, during the early flowering period (myfs3), the flowers are blue–purple, which is a rare colour. Delphinidin is the first type of anthocyanin that accumulates in the pigment synthesis pathway of blue–violet flowers and is the most important basal pigment[26]. Malvidin and petunidin, which represent different degrees of delphinidin methylation, are also important colour-forming substances for many purple plants[27,28]. Mizuta suggested that blue anthocyanins are dominated by delphinidin, malvidin, and petunidin[29]. In this study, petunidin-3,5-O-diglucoside, petunidin-3-O-glucoside, and delphinidin-3-O-glucoside expression levels began to increase at the myfs1 stage and reached their highest levels during the early flowering period (myfs3). These results suggest that these three metabolites may be involved in the development of flower colour during the early flowering period, resulting in a blue‒purple colour.

      However, the flowers of C. lanuginosa in the myfs3 stage are not only pure blue‒purple but also purple‒red. Previous reports have indicated that cyanidin produces a magenta flower colour, whereas pelargonidin produces a brick-red flower colour[30,31]. In this study, cyanidin-3,5,3'-O-triglucoside, cyanidin-3,5-O-diglucoside, cyanidin-3-O-rutinoside-5-O-glucoside, and pelargonidin-3-O-rutinoside-5-O-glucoside were upregulated in the myfs3 stage, indicating that they may be important compounds for flower colour formation in C. lanuginosa and, in combination with the abovementioned metabolites, produce blue–purple flowers.

      In addition, in the end flower development stage (myfs6), cyanidin-3,5,3'-O-triglucoside, cyanidin-3-O-rutinoside-5-O-glucoside, delphinidin-3-O-glucoside, pelargonidin-3-O-rutinoside-5-O-glucoside, petunidin-3,5-O-diglucoside and petunidin-3-O-glucoside were significantly downregulated. The loss of these metabolites may cause the colour of C. lanuginosa to become lighter, as the flower colour gradually becomes white in later stages[32].

    • Flowering time is an important life history trait in plants and is regulated by both internal and environmental factors[33,34]. Transcription factors mainly regulate the transcription of structural genes and thus participate in anthocyanin biosynthesis. By binding to cis-acting elements in the promoters of structural genes, transcription factors activate or inhibit the expression of one or more structural genes in the anthocyanin biosynthesis pathway, and functional proteins can coordinate these interactions[35,36]. Jin et al. reported that the MYB transcription factor PavMYB10.1 participates in anthocyanin biosynthesis in sweet cherry, thereby affecting fruit colour[37]. Notably, MdMYB10 participates in anthocyanin synthesis and regulates fruit colour[38]. Stracke et al. reported that in the model plant Arabidopsis, the transcription factor MYB can regulate the expression of early structural genes such as FLS, CHI, F3H, and CHS[39].

      In addition, previous studies have reported that ternary protein complexes formed by the MYB, bHLH, and WD40 genes play important roles in regulating anthocyanin biosynthesis[40,41]. In Arabidopsis, the MBW transcription complex WD40 was found to interact with different MYB transcription factors[42]. In the MBW ternary complex, MYB and bHLH transcription factor interactions are prerequisites for the recognition of specific DNA sequences. Moreover, WD40 transcription factors can increase the stability of ternary complexes[43]. Therefore, the MBW ternary complex, like other transcription factors, acts mainly by regulating the transcriptional abundance of structural genes, and simultaneously, each member can also coordinate with the others[44]. In a study in Arabidopsis, Li reported that the MYB–bHLH–WD40 (MBW) complex activated late anthocyanin biosynthetic genes[45]. Zhao et al. reported that FaMYB9/FaMYB11, FabHLH3, and FaTTG1 are functional homologues of AtTT2, AtTT8, and AtTTG1 in strawberry and promote strawberry fruit growth[42]. Feller et al. reported that the interaction between bHLH and R2R3-MYB proteins plays an important role in the colour production process of maize[46]. In this study, 15 differentially expressed MYB genes, eight bHLH genes, and 26 WD40 genes were identified, and relevant transcription factors were also predicted. Changes in the anthocyanin content of C. lanuginosa flowers may be related to and caused by these transcription factors. Specifically, the combined metabolic‒transcriptional analysis suggested that the MYB gene (transcript41913_f2p0_1152) may be a key gene involved in the development of C. lanuginosa flower colour.

    • To further study colour development in C. lanuginosa flowers, in this study, metabolome and transcriptome sequencing was performed at six flower development stages (myfs1, myfs2, myfs3, myfs4, myfs5, and myfs6). The metabolome sequencing results revealed 25 anthocyanin compounds, including 22 differentially expressed metabolites. Cyanidin-3,5-O-diglucoside, cyanidin-3,5,3'-O-triglucoside, cyanidin-3-O-rutinoside-5-O-glucoside, delphinidin-3-O-glucoside, and petunidin-3-O-glucoside may affect the formation of blue–purple flowers in C. lanuginosa. The combined metabolome and transcriptome analysis results revealed that the MYB gene (transcript41913_f2p0_1152) is a key gene involved in C. lanuginosa flower colour development and change.

      • This research was supported by the National Natural Science Foundation of China (32102428); the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding (2021C02071-6); Wenzhou Agricultural New Variety Breeding Cooperative Group Project (ZX2024004-3).

      • The authors confirm contribution to the paper as follows: experiments design: Ye Y, Qian R; experiments performing: Ye Y, Qian R, Hu Q, Ma X; manuscript preparation: Ye Y, Qian R, Gao H, Zheng J. All authors reviewed the results and approved the final version of the manuscript.

      • The data produced in the study were uploaded to the NCBI database (BioProject: PRJNA1117522, SRA: SRP510351).

      • The authors declare that they have no conflict of interest.

      • # Authors contributed equally: Renjuan Qian, Youju Ye

      • Copyright: © 2025 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (7)  Table (2) References (46)
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    Qian R, Ye Y, Ma X, Gao H, Hu Q, et al. 2025. Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers. Ornamental Plant Research 5: e001 doi: 10.48130/opr-0024-0031
    Qian R, Ye Y, Ma X, Gao H, Hu Q, et al. 2025. Targeted metabolome and transcriptome analysis reveals the key metabolites and genes influencing blue–purple colour development in Clematis lanuginosa flowers. Ornamental Plant Research 5: e001 doi: 10.48130/opr-0024-0031

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