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2023 Volume 3
Article Contents
Abstract
Maize is a major staple crop and a model species for plant biology and genetic studies
Hundreds of genomic regions were selected during maize domestication
Domestication genes for morphological traits
Teosinte branched1 (tb1) and increased apical dominance in maize
Teosinte glume architecture 1 (tga1) and reduced glumes in maize
Grassy tillers1 (gt1) and suppression of prolificacy in maize
UPA2 and leaf angle
GLOSSY15 (Gl15) and vegetative phase change
Domestication genes related to identity and morphology of inflorescence
Domestication genes of kernel-related traits
ZmSh1 and seed shattering
Nutritional quality
Domestication genes related to disease resistance and abiotic stresses
Pre-Columbia spread of maize from tropical to temperate
Selective genes underlying agronomic trait improvement during modern maize breeding
Key genes/pathways regulating shade avoidance responses and plant architecture in maize
Complementary and differential selection of heterotic groups during modern maize breeding
Perspectives
Acknowledgments
Conflict of interest
Supplementary information
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Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China

  • 1.

    College of Horticulture, Fujian Agriculture and Forestry University/Key Laboratory of Tea Science in Universities of Fujian Province, Fuzhou 350002, China

  • 2.

    College of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi, China

  • 3.

    Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 362406, China

  • # These authors contributed equally: Ting Gao, Shuxian Shao

More Information
  • The volatiles in the young shoots of tea cultivars are the important material basis for the formation of tea aroma, but the cultivar-specific aroma and its molecular regulation are still lacking in research. In this study, the characteristic volatiles of seven tea cultivars in China were detected, and the results showed that the green tea cultivars 'Fuding Dabaicha' (FDDB), 'Longjing43' (LJ43), 'Shuchazao' (SCZ), and 'Baihaozao' (BHZ) were rich in (E)-3-hexenol, phenylethyl alcohol, phenylacetaldehyde, and β-ionone. For oolong tea cultivars, the characteristic volatiles of 'Tieguanyin' (TGY) were heptanal and eugenol, while the contents of (E)-β-ocimene, geraniol, and methyl salicylate were significantly increased in 'Jinxuan' (JX). In addition, 'Fujian Shuixian' (FJSX) has the highest content of esters, mainly jasmonolactone and dihydrojasmonolactone. Transcriptomic analysis showed that the different tea cultivars were significantly enriched in different levels of gene transcription in the three pathways related to aroma biosynthesis. Potential regulatory modules and genes of several characteristic volatiles were identified by WGCNA, among which CsbHLH (CsTGY12G0001520) may regulate the expression of CsTPS (CsTGY05G0001285) to directly affect the accumulation of β-caryophyllene in young shoots, while CsMYB (CsTGY01G0001203, CsTGY04G0001918, CsTGY06G0002545) may affect the synthesis of (Z)-3-hexenol and (E)-3-hexen-1-ol acetate by regulating the CsADH (CsTGY09G0001879). In addition, the transcription factors bHLH, WRKY, ERF, and MYB may be involved in the biosynthesis of linalool by regulating the expression of CsLIS/NES (CsTGY08G0001704, CsTGY08G0000359) genes individually or through interaction. These results revealed the characteristic volatiles and their key regulatory genes of seven tea cultivars, which will provide a theoretical basis for breeding and suitability research of tea cultivars.

  • Maize is a major staple crop and a model species for plant biology and genetic studies

    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[4−6]. 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].

    Hundreds of genomic regions were selected during maize domestication

    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.
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    Domestication genes for morphological traits

    Teosinte branched1 (tb1) and increased apical dominance in maize

    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 glume architecture 1 (tga1) and reduced glumes in maize

    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].

    Grassy tillers1 (gt1) and suppression of prolificacy in maize

    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.

    UPA2 and leaf angle

    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].

    GLOSSY15 (Gl15) and vegetative phase change

    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].

    Domestication genes related to identity and morphology of inflorescence

    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.

    Domestication genes of kernel-related traits

    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.

    ZmSh1 and seed shattering

    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].

    Nutritional quality

    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].

    Domestication genes related to disease resistance and abiotic stresses

    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.[64−66] 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[18−22]
    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;[44−46]
    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]
     | Show Table
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    Pre-Columbia spread of maize from tropical to temperate

    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[77−79]. 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[72,81−83].

    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].
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    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[77−79]
    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]
     | Show Table
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    Selective genes underlying agronomic trait improvement during modern maize breeding

    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.

    Key genes/pathways regulating shade avoidance responses and plant architecture in maize

    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[101−103]. 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]
     | Show Table
    DownLoad: CSV

    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.

    Complementary and differential selection of heterotic groups during modern maize breeding

    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[114−116]. Multiple studies with molecular markers have suggested that heterotic groups have diverged genetically over time for better heterosis[117−120]. 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.

    Perspectives

    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[124−126]. 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!

    Acknowledgments

    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.

  • Supplemental Table S1 The primers for qRT-PCR.
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    Gao T, Shao S, Hou B, Hong Y, Ren W, et al. 2023. Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China. Beverage Plant Research 3:17 doi: 10.48130/BPR-2023-0017
    Gao T, Shao S, Hou B, Hong Y, Ren W, et al. 2023. Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China. Beverage Plant Research 3:17 doi: 10.48130/BPR-2023-0017
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Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China

  • 1.

    College of Horticulture, Fujian Agriculture and Forestry University/Key Laboratory of Tea Science in Universities of Fujian Province, Fuzhou 350002, China

  • 2.

    College of Horticulture, Northwest A&F University, Yangling 712100, Shaanxi, China

  • 3.

    Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 362406, China

  • Received: 12 April 2023
  • Revised: 14 June 2023
  • Accepted: 19 June 2023
  • Published online: 18 July 2023
Beverage Plant Research  3,  Article number: 17  (2023)  |  Cite this article

Abstract: The volatiles in the young shoots of tea cultivars are the important material basis for the formation of tea aroma, but the cultivar-specific aroma and its molecular regulation are still lacking in research. In this study, the characteristic volatiles of seven tea cultivars in China were detected, and the results showed that the green tea cultivars 'Fuding Dabaicha' (FDDB), 'Longjing43' (LJ43), 'Shuchazao' (SCZ), and 'Baihaozao' (BHZ) were rich in (E)-3-hexenol, phenylethyl alcohol, phenylacetaldehyde, and β-ionone. For oolong tea cultivars, the characteristic volatiles of 'Tieguanyin' (TGY) were heptanal and eugenol, while the contents of (E)-β-ocimene, geraniol, and methyl salicylate were significantly increased in 'Jinxuan' (JX). In addition, 'Fujian Shuixian' (FJSX) has the highest content of esters, mainly jasmonolactone and dihydrojasmonolactone. Transcriptomic analysis showed that the different tea cultivars were significantly enriched in different levels of gene transcription in the three pathways related to aroma biosynthesis. Potential regulatory modules and genes of several characteristic volatiles were identified by WGCNA, among which CsbHLH (CsTGY12G0001520) may regulate the expression of CsTPS (CsTGY05G0001285) to directly affect the accumulation of β-caryophyllene in young shoots, while CsMYB (CsTGY01G0001203, CsTGY04G0001918, CsTGY06G0002545) may affect the synthesis of (Z)-3-hexenol and (E)-3-hexen-1-ol acetate by regulating the CsADH (CsTGY09G0001879). In addition, the transcription factors bHLH, WRKY, ERF, and MYB may be involved in the biosynthesis of linalool by regulating the expression of CsLIS/NES (CsTGY08G0001704, CsTGY08G0000359) genes individually or through interaction. These results revealed the characteristic volatiles and their key regulatory genes of seven tea cultivars, which will provide a theoretical basis for breeding and suitability research of tea cultivars.

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    Introduction

    • Tea aroma is affected by the tea cultivars, growing environment, and manufacturing process, and is an essential factor in determining the quality of tea[1]. As a high-weight trait in tea plant breeding, the volatile content of tea cultivars with different adaptability is usually different[2,3]. Studies have shown that the contents and ratios of linalool and geraniol are genetically specific and stable in tea cultivars[4]. The aroma quality of oolong tea is related to the release of aroma glycosides in the leaves of cultivars during the shaking process[5]. The major volatiles in tea are derived from either the terpenoid and shikimate pathways or by the oxidation of fatty acids and carotenoids[6]. It was found that aroma components can be synthesized by a single gene or multiple gene interactions[7]. Moreover, many transcription factor (TFs) can also participate in the formation of volatiles by regulating the expression of aroma synthesis pathway genes[8]. In summary, the formation and regulation of tea aroma is a complex process.

      Oolong tea, which is a semifermented tea, possesses an elegant floral odour and is gaining popularity in China due to its distinct and characteristic aromas. Furthermore, the aroma quality of oolong tea can vary greatly because of cultivars, tea manufacturing process, regions, climate conditions, season of harvest, and quality of fresh tea leaves, with the cultivar being the most important factor[2, 3]. 'Tieguanyin' (TGY, Registration No. GS13007-1985) tea is a typical cultivar of Chinese oolong tea, which is famous for its unique rich flavour and orchid-like aroma[2, 9, 10]. 'Jinxuan' (JX, Registration No. MS2011002) is one of the main cultivated tea cultivars in Fujian Province, China. Oolong tea processed from JX is popular among tea drinkers due to its unique floral and creamy aroma[11]. 'Fujian Shuixian' (FJSX, Registration No. GS13009-1985) is considered to be one of the most suitable cultivars for producing oolong tea[12]. Oolong tea processed from 'Shuixian' is popular among tea drinkers due to its sharp and typical floral odour[13]. White tea is a lightly fermented tea popular for its sweetness, clear fragrance, mellow aroma and outstanding health benefits, and it is rich in volatile compounds inherent to fresh leaves, such as aldehydes and alcohols[14, 15]. 'Fuding Dabaicha' (FDDB, Registration No. GS13001-1985) is a major cultivar suitable for making white tea and an important parent for breeding green and black tea cultivars, which played important roles in the Chinese tea breeding history[16]. Chinese green tea, the most popular tea in China, presents different characteristic aroma types according to its sensory quality, such as floral, green, and delicate aromas[17]. For instance, the representative green tea cultivars 'Longjing43' (LJ43, Registration No. GS 13037-1987), 'Baihaozao' (BHZ, Registration No. GS13017-1994), and 'Shuchazao' (SCZ, Registration No GS2002008), which come from Fujian, Zhejiang, Hunan, and Anhui Provinces, respectively, were identified as Chinese national improved cultivars.

      In our previous study[18], we have analyzed the characteristic metabolites of seven tea cultivars using targeted metabolomics and widely targeted metabolomics, and combined transcriptome data to construct transcriptional regulatory networks for the characteristic metabolites of different cultivars. In addition to non-volatile metabolites, tea cultivars also affect the content of aromatic substances in its fresh leaves, which are the material basis for the formation of tea aroma. Although there have been many studies on the volatile components of tea, most of them have focused on tea processing and finished tea[19−21], and the influence of tea cultivars on aroma formation has received little attention. In this study, volatile metabolomics and transcriptomics were used to analyze the characteristic aroma components and differential genes of seven tea cultivars. Then the transcriptome data and aroma components were correlated by weighted gene co-expression network analysis (WGCNA), and co-expressed gene modules were screened to construct transcriptional regulatory networks of characteristic aroma components. These data and results will provide a theoretical basis for the production adaptability of tea cultivars at the aroma component and molecular level.

    Materials and methods

      Tea plant materials

    • In April 2021, the young shoots (one bud and two leaves) of the tea plants of Camellia sinensis (L.) O. Kuntze 'Tieguanyin' (TGY), 'Jinxuan' (JX), 'Fujian Shuixian' (FJSX), 'Fuding Dabaicha' (FDDB), 'Baihaozao' (BHZ), 'Longjing 43' (LJ43), and 'Shuchazao' (SCZ) were collected from the tea germplasm plantation of Wuyi University (Wuyishan City, Fujian, China; 27°73′17″ N, 118°00′18″ E) for detection of released volatiles and transcriptome analysis. Indeed, all tea plants were grown under the same cultivation practices. Three independent biological replicates were set up. The collected samples were immediately frozen with liquid nitrogen and stored in a freezer at −80 °C.

      Analysis of volatile metabolites

    • The method for determining and analysing volatile metabolites was consistent with our previous report[2]. In brief, the samples were ground into powder in liquid nitrogen, and then 1 g of the powder was immediately transferred to a 20 mL Agilent headspace vial (CA, USA) containing saturated NaCl and 10 µL (50 µg/mL) [2H3]-β-ionone internal standard solution. After 5 min of constant temperature at 100 °C, 120 μm DVB/CAR/PDMS extraction head was inserted into the headspace bottle, and the headspace extraction was carried out for 15 min, and the sample was analyzed at 250 °C for 5 min. The volatile metabolites were detected using an Agilent Model 8890 GC and a 5977B mass spectrometer (Agilent). The analytical conditions were set as follows: desorption of the volatiles from the fibre coating at 250 °C for 5 min in the splitless mode. The carrier gas was helium, and the linear velocity was 1.0 mL/min. The temperature of the injector was kept at 250 °C, and the temperature of the detector was kept at 280 °C. Mass spectra were recorded in electron impact ionization mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set at 150, 230, and 280 °C, respectively. Mass spectra were scanned in the range m/z 50−500 amu at 1 s intervals.

      Detection of volatile compounds

    • Volatile metabolites were identified by comparing the mass spectra with the data system library (MWGC or NIST) and the linear retention index. Each sample was repeated three times, and the data are expressed as the mean ± standard deviation. The concentrations of volatile compounds in tea plants were quantified based on their peak areas and the peak area of the internal standard compound. The bar charts were made by Excel, and the line charts were made by GraphPad Prism 9.0. Analysis of variance and significant difference analysis were performed by SPSS 26.0. Principal component analysis (PCA) of the identified metabolites was performed using the R package (www.r-project.org). Based on the variable importance in project (VIP) score obtained by the OPLS-DA model, metabolites with VIP ≥ 1.0 and fold change (FC) ≥ 1.5 or FC ≤ 0.67 were defined as significantly changed metabolites (SCMs). The calculation method for the odour activity values (OAVs) was the same as that used in a previous study. OAV = C/OT, where C is the concentration of the volatile compound and OT is its odour threshold[22]. Compounds with OAV ≥ 1 were considered potential contributors to the tea aroma profile.

      Transcriptome-based analysis of aroma component-related pathways and differentially expressed genes

    • The transcriptome data was based on contemporaneous data that we previously published[18]. All RNA-seq data are publicly available in the BIG Data Center (https://bigd.big.ac.cn) under project number PRJCA009753. Differential expression analysis with DESeq2 software. Genes with |log2FC| ≥ 1 and p-value < 0.05 were considered to be differentially expressed genes (DEGs) by the DESeq2 R package[23]. All the genes assembled by the transcriptome were compared with six databases (NR, Swiss-Prot, Pfam, EggNOG, GO, and KEGG) to obtain the functional information of genes, and the annotation of each database was statistically analysed. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the ClusterProfiler v4.0.0 R package[24] and BLAST was set to e-value ≤ 10−5. For TPS gene, the hidden Markov models of PF01397 and PF03936 were downloaded from Pfam (http://pfam.xfam.org/) database, and HMMER software was used to search the TPS gene family sequence. The BLASTP of NCBI and Swiss-Prot was used to predict the possible function of CsTPS gene, and the threshold was set as E-value < 10−5 and identity > 90%. For transcription factors (TFs), all the genes were annotated in the Plant Transcription Factor Database (PlantTFDB v5.0) to determine whether they are TFs. TBtools software was used to make a heatmap for visualization of DEGs.

      Gene coexpression analysis

    • Weighted gene coexpression network analysis (WGCNA) was performed using the WGCNA R package. Genes with TPM > 1 and coefficient of variation (cv) > 0.1 were used to construct the coexpression network. After filtering, the abundance of 11,222 genes and 20 metabolites was used to build a signed coexpression network by calculating Pearson correlations. The soft-thresholding power of the correlation network was set to 14, the minimum module size was equal to 30, and the minimum height for merging modules was set to 0.5. The module networks were visualized using Gephi software.

      Quantitative real-time PCR verification

    • cDNA synthesis and qRT‒PCR tests were performed to verify the reliability of the RNA-Seq data according to previous methods[25]. CsGAPDH (GE651107) was used as a reference control, and the primers of validated genes were designed using Primer3Plus (www.primer3plus.com). The primer information is listed in Supplemental Table S1. All samples were analysed in three biological replicates. The relative expression level was calculated using the 2−ΔΔCᴛ method[26].

    Results

      The volatile components of seven tea cultivars

    • In total, 88 volatiles were identified by GC–MS in the seven tea cultivars (Table 1), including alcohols (22), phenols (3), aldehydes (12), acids (5), terpenoids (13), ketones (7), hydrocarbons (3), heterocyclic compounds (3) and esters (20). Alcohols and esters accounted for 47.72% of the total aroma content, among which geraniol, linalool, methyl salicylate and (E)-3-hexen-1-ol acetate had higher contents. There were 19 volatiles with relative contents greater than 100 µg/kg. Geraniol and linalool contain more than 1,000 µg/kg, accounting for 1%−5% of the total content. The total contents of volatiles in the seven tea cultivars were in the following order: JX > FDDB > SCZ > FJSX > LJ43 > TGY > BHZ.

      Table 1.  The volatile components of seven tea cultivars.

      IDCASRT /minCompoundsRelative contents (µg/kg)
      TGYJXFJSXFDDBBHZLJ43SCZ
      1106-24-114.27Geraniol1,358.90 ± 49.69d5103.36 ± 85.9a3,017.74 ± 80.37b2,573.91 ± 69.41c239.72 ± 10.4e1,321.78 ± 27.49d2,510.78 ± 62.78c
      278-70-611.46Linalool1,012.66 ± 10.81d1,258.39 ± 74.46bc1,106.32 ± 71.3cd2,465.09 ± 167.61a478.07 ± 44.92e1,212.96 ± 18.69bcd1,408.27 ± 132.44b
      340716-66-319.76(E)-Nerolidol54.73 ± 2.25c54.71 ± 0.86c72.67 ± 5.77b34.06 ± 1.59d107.39 ± 4.41a67.08 ± 3.28b115.07 ± 5.96a
      4106-25-213.75Nerol22.42 ± 0.62e72.25 ± 1.44a49.45 ± 4.07cd60.73 ± 1.75b11.29 ± 0.31f35.87 ± 0.79d60.29 ± 3.10b
      5928-96-16.67(Z)-3-Hexenol34.90 ± 0.64d33.82 ± 1.24d79.71 ± 11.02c177.94 ± 10.84b73.79 ± 9.13c170.01 ± 4.20b229.24 ± 26.97a
      6100-51-612.50Benzyl alcohol0.28 ± 0.02a0.16 ± 0.03cd0.16 ± 0.03bcd0.22 ± 0.03ab0.12 ± 0.01d0.21 ± 0.00bc0.17 ± 0.02bcd
      760-12-811.69Phenylethyl alcohol95.04 ± 6.81e114.96 ± 1.21e15.49 ± 0.09f739.79 ± 17.52a414.46 ± 7.66c338.25 ± 5.13d689.34 ± 36.16b
      8505-32-823.78Isophytol10.36 ± 2.57b9.28 ± 1.52b12.55 ± 2.90b7.46 ± 2.43b24.32 ± 8.12a8.23 ± 3.84b14.38 ± 3.96ab
      9150-86-724.63Phytol11.05 ± 3.74ab9.64 ± 1.83ab18.00 ± 6.78ab7.29 ± 3.37b30.89 ± 18.38a6.36 ± 4.45b15.20 ± 9.07ab
      1029171-23-124.77Dehydroisophytol2.73 ± 0.71b1.83 ± 0.39b3.36 ± 1.03ab1.71 ± 0.63b5.53 ± 1.83a1.73 ± 0.93b2.86 ± 0.96b
      11143-08-812.741-Nonanol10.40 ± 0.33c8.33 ± 0.59cd6.59 ± 0.56d29.29 ± 0.77a21.15 ± 1.31b31.58 ± 1.08a31.27 ± 2.04a
      12111-70-69.031-Heptanol6.53 ± 0.27d5.88 ± 0.27d3.77 ± 0.51d19.03 ± 1.98a11.75 ± 1.10bc10.68 ± 0.49c13.75 ± 2.12b
      1310482-56-113.24α-Terpineol26.66 ± 0.24a26.81 ± 1.26a19.35 ± 1.44b24.79 ± 1.22a6.21 ± 0.53e10.88 ± 0.71d14.51 ± 1.87c
      1410340-23-512.43(Z)-3-Nonen-1-ol0.97 ± 0.13d1.44 ± 0.12bc1.22 ± 0.16cd2.26 ± 0.09a1.27 ± 0.05cd2.34 ± 0.17a1.75 ± 0.21b
      152425-77-622.132-Hexyl-1-decanol2.22 ± 0.05a1.22 ± 0.11d1.85 ± 0.35bc1.63 ± 0.08c2.05 ± 0.07ab1.64 ± 0.02c1.91 ± 0.02bc
      16481-34-521.18α-Cadinol3.08 ± 0.08d11.32 ± 0.47a4.30 ± 0.28c1.89 ± 0.13e5.97 ± 0.16b2.18 ± 0.09e1.67 ± 0.14e
      175944-20-713.83Isogeraniol5.75 ± 0.21e7.19 ± 0.26e23.45 ± 2.80b16.03 ± 1.19c11.98 ± 0.87d30.30 ± 0.99a14.74 ± 1.49cd
      18498-00-014.09Vanillyl alcohol246.47 ± 2.27b242.40 ± 4.17b251.82 ± 8.47ab257.91 ± 7.73ab243.94 ± 6.00b265.31 ± 4.52a244.78 ± 11.91b
      1915051-81-719.43epi-g-Eudesmol1.09 ± 0.10d0.90 ± 0.09de1.13 ± 0.10cd0.66 ± 0.06e1.76 ± 0.14ab1.43 ± 0.13bc1.79 ± 0.29a
      2018675-33-715.29(+)-Neodihydrocarveol2.54 ± 0.12a1.61 ± 0.06b0.79 ± 0.04d0.49 ± 0.02e1.02 ± 0.11c0.00 ± 0.00f0.00 ± 0.00f
      215989-33-310.90(Z)-Linalool oxide (furanoid)26.59 ± 1.08e97.88 ± 5.72d43.45 ± 4.25e255.98 ± 13.68a84.79 ± 6.13d202.69 ± 13.96b130.23 ± 11.66c
      2234995-77-211.20(E)-Linalool oxide (furanoid)81.38 ± 4.35e314.5 ± 19.75c81.20 ± 5.77e666.69 ± 29.84a189.5 ± 12.55d408.19 ± 31.09b299.17 ± 27.17c
      2339028-58-512.84(E)-Linalool oxide (pyranoid)15.78 ± 0.36d40.13 ± 2.58b12.91 ± 0.95d69.64 ± 0.99a39.31 ± 2.55b34.67 ± 1.30c36.72 ± 1.82bc
      Alcohols (23)3,032.53 ± 74.37e7,418.01 ± 149.51a4,827.26 ± 203.54c7,414.48 ± 291.74a2,006.27 ± 48.02f4,164.36 ± 50.25d5,837.9 ± 253.16b
      2497-53-016.12Eugenol3.17 ± 0.58a0.73 ± 0.01c1.82 ± 0.44b1.67 ± 0.13b0.95 ± 0.05c1.94 ± 0.04b2.01 ± 0.14b
      253228-02-215.133-Methyl-4-isopropylphenol5.46 ± 1.38a1.03 ± 0.11c3.14 ± 0.68b1.82 ± 0.07bc0.76 ± 0.12c0.99 ± 0.15c0.87 ± 0.18c
      Phenols (2)8.63 ± 1.85a1.76 ± 0.12c4.96 ± 1.11b3.49 ± 0.09bc1.71 ± 0.14c2.93 ± 0.14c2.88 ± 0.29c
      26141-27-514.55Neral25.34 ± 1.36c93.8 ± 3.25a60.48 ± 7.92b65.70 ± 7.92b5.30 ± 0.95d37.78 ± 1.06c59.69 ± 6.84b
      27121-33-513.69Vanillin3.25 ± 0.28d0.74 ± 0.11e10.16 ± 2.30a5.28 ± 0.45bcd3.90 ± 0.21cd6.27 ± 0.86b6.15 ± 0.11bc
      2834246-54-312.673-Ethylbenzaldehyde0.65 ± 0.01cd0.62 ± 0.02de0.42 ± 0.05e0.70 ± 0.07cd0.86 ± 0.12bc1.01 ± 0.02ab1.10 ± 0.11a
      296728-26-314.46(E)-2-Hexenal3.25 ± 0.48cd5.99 ± 0.58a4.38 ± 0.19b4.10 ± 0.31bc1.93 ± 0.12e3.16 ± 0.37d4.01 ± 0.36bcd
      30100-52-78.87Benzaldehyde9.82 ± 0.70ab8.20 ± 0.37bc6.17 ± 1.08c8.72 ± 1.21bc9.00 ± 1.21bc11.07 ± 0.73ab12.86 ± 2.30a
      31122-78-111.69Phenylacetaldehyde99.41 ± 7.28e121.13 ± 1.40e18.87 ± 0.40f746.69 ± 12.32a435.00 ± 6.55c356.40 ± 14.09d700.08 ± 30.99b
      32112-31-213.40Decanal0.59 ± 0.07b0.64 ± 0.15b0.69 ± 0.07b0.82 ± 0.17b0.86 ± 0.14b1.19 ± 0.12a0.81 ± 0.04b
      33111-71-77.67Heptanal143.12 ± 4.70a2.18 ± 0.24b2.16 ± 0.41b1.60 ± 0.24b1.15 ± 0.19b1.09 ± 0.11b0.90 ± 0.11b
      3466-25-15.45Hexanal7.83 ± 1.19d15.44 ± 3.43bcd12.48 ± 1.33cd18.63 ± 2.71bc27.58 ± 6.03a29.47 ± 3.54a22.46 ± 1.58ab
      35123-72-85.45Butanal6.59 ± 1.30d14.07 ± 3.66bcd11.59 ± 1.53cd16.53 ± 1.67bc26.64 ± 5.32a28.38 ± 3.38a21.63 ± 2.66ab
      36110-62-34.83Valeraldehyde14.41 ± 1.13de20.84 ± 1.88cd13.41 ± 2.76e24.21 ± 1.66c33.52 ± 2.5ab31.13 ± 1.22b40.15 ± 5.75a
      37590-86-35.03Isovaleraldehyde48.88 ± 1.45a21.11 ± 1.69b33.19 ± 5.46ab37.00 ± 5.6ab34.38 ± 8.89ab45.65 ± 1.09a39.15 ± 11.95a
      Aldehydes (12)363.15 ± 9.42c304.75 ± 8.93c174.01 ± 3.96d929.96 ± 12.47a580.11 ± 28.65b552.59 ± 8.81b908.99 ± 56.08a
      38459-80-316.02Geranic acid37.94 ± 9.28bc115.49 ± 33.01a62.74 ± 23.82b20.81 ± 3.51bc8.82 ± 2.85c19.69 ± 2.49c131.88 ± 17.95a
      39112-05-014.48Nonanoic acid2.58 ± 0.66c4.45 ± 1.22ab4.80 ± 0.65ab4.39 ± 0.67ab5.51 ± 0.47a3.51 ± 0.44bc4.64 ± 0.40ab
      40111-14-812.69Heptanoic acid119.89 ± 0.98a101.69 ± 5.30cd115.81 ± 5.88ab107.27 ± 3.55bcd98.31 ± 5.48d111.4 ± 2.49abc104.84 ± 6.82bcd
      41109-52-414.48Pentanoic acid4.92 ± 0.63b7.14 ± 1.45ab8.07 ± 1.02a8.00 ± 1.74a8.12 ± 1.10a6.55 ± 0.54ab7.57 ± 0.45ab
      4279-09-412.05Propanoic acid65.13 ± 26.72a79.82 ± 37.7a89.19 ± 28.57a50.37 ± 21.63a65.56 ± 23.49a64.99 ± 18.33a67.71 ± 24.17a
      Acids (5)230.47 ± 31.08ab308.59 ± 71.53a280.6 ± 47.71ab190.84 ± 20.11b186.32 ± 20.63b206.13 ± 16.59b316.64 ± 32.43a
      43127-41-39.92α-Ionone12.05 ± 0.88cd23.28 ± 0.54a14.7 ± 1.06bc17.81 ± 1.66b2.60 ± 0.43e10.34 ± 1.64d14.11 ± 2.26c
      4479-77-618.39β-Ionone87.2 ± 1.64c90.97 ± 0.81c92.93 ± 7.34c103.48 ± 5.56b111.25 ± 4.28ab108.43 ± 2.05ab114.18 ± 2.52a
      45689-67-817.79Geranylacetone10.67 ± 0.30bc12.01 ± 0.49a11.15 ± 1.07abc10.07 ± 0.57c11.79 ± 0.19ab10.58 ± 0.05bc11.43 ± 0.29ab
      46502-69-223.10Fitone6.01 ± 0.21bc4.82 ± 0.37de6.70 ± 0.61b7.05 ± 0.24b9.15 ± 0.70a4.17 ± 0.21e5.51 ± 0.61cd
      47471-15-811.623-Thujone11.55 ± 3.73a1.49 ± 1.68b6.56 ± 2.92ab0.84 ± 0.34b4.92 ± 4.11b2.18 ± 1.52b0.65 ± 0.44b
      48488-10-816.84Jasmone6.64 ± 0.35d4.79 ± 0.61e4.24 ± 0.28e5.18 ± 0.26e9.53 ± 0.41b7.84 ± 0.53c12.43 ± 0.39a
      4923726-93-416.53β-Damascenone1.01 ± 0.15e18.05 ± 0.75a14.26 ± 1.60b7.58 ± 0.13cd1.82 ± 0.24e6.11 ± 0.14d9.02 ± 0.31c
      Ketones (7)135.12 ± 4.62c155.41 ± 3.29ab150.53 ± 12.41bc152.02 ± 6.26ab151.07 ± 7.13bc149.65 ± 0.72bc167.33 ± 4.26a
      50544-76-320.41Hexadecane3.25 ± 0.34a4.04 ± 0.38a3.41 ± 0.90a4.73 ± 1.33a4.82 ± 0.68a4.11 ± 0.54a4.26 ± 0.56a
      51629-59-416.97Tetradecane7.30 ± 00.43ab5.87 ± 0.54b6.21 ± 0.46b7.82 ± 1.40ab7.82 ± 0.95ab8.41 ± 0.70a7.27 ± 0.84ab
      523891-99-418.032,6,10-Trimethyltridecane6.51 ± 0.12bc5.13 ± 0.72c6.91 ± 2.37abc6.36 ± 0.50bc8.50 ± 0.66ab8.21 ± 0.16ab9.67 ± 1.60a
      Hydrocarbons (3)17.07 ± 0.59ab15.04 ± 1.09b16.54 ± 3.61ab18.9 ± 2.56ab21.14 ± 2.03a20.72 ± 1.21ab21.2 ± 2.99a
      53120-72-915.05Indole0.00 ± 0.00f36.36 ± 1.23b0.00 ± 0.00f11.8 ± 0.78d42.41 ± 1.36a7.78 ± 0.62e24.75 ± 2.46c
      5491-64-517.72Coumarin16.81 ± 1.45a18.38 ± 0.34a17.67 ± 1.54a11.5 ± 0.53b10.28 ± 0.58b11.43 ± 0.48b10.50 ± 0.61b
      5536431-72-815.22Theaspirane3.92 ± 0.05c10.98 ± 0.92b4.57 ± 0.49c14.31 ± 1.95a5.88 ± 0.24c9.10 ± 0.19b4.23 ± 0.44c
      Heterocyclic compounds (3)20.73 ± 1.41f65.72 ± 0.98a22.24 ± 1.79f37.61 ± 2.32d58.57 ± 0.92b28.31 ± 0.70e39.48 ± 2.71c
      566753-98-618.06α-Humulene6.58 ± 0.40a4.63 ± 0.11b1.16 ± 0.04e1.19 ± 0.07e3.06 ± 0.21d0.86 ± 0.04e3.68 ± 0.42c
      5787-44-517.43β-Caryophyllene14.30 ± 0.46a14.97 ± 0.30a2.04 ± 0.28d4.54 ± 0.73c8.49 ± 0.48b0.50 ± 0.19e1.62 ± 0.26d
      58502-61-418.79α-Farnesene28.28 ± 0.62c34.79 ± 0.54b12.15 ± 1.56e14.98 ± 1.35e43.86 ± 1.15a12.50 ± 0.69e24.02 ± 1.91d
      59123-35-39.39Myrcene241.44 ± 15.17c669.11 ± 7.54a374.82 ± 41.18b426.14 ± 44.52b44.16 ± 6.77d230.59 ± 33.5c363.94 ± 50.74b
      603779-61-110.44(E)-β-Ocimene107.36 ± 5.92c263.91 ± 3.58a147.98 ± 15.26b174.1 ± 17.53b23.92 ± 4.42d99.25 ± 13.74c155.4 ± 25.28b
      617216-56-011.95Allo-ocimene14.16 ± 1.00c37.51 ± 0.65a21.17 ± 2.50b23.85 ± 2.91b2.43 ± 0.34d12.92 ± 2.22c20.17 ± 2.98b
      6299-85-410.68γ-Terpinene11.22 ± 1.12b17.20 ± 0.51a12.16 ± 0.68b13.79 ± 1.33b2.42 ± 0.36d8.25 ± 1.30c11.29 ± 2.04b
      635208-59-316.78β-Bourbonene4.85 ± 0.11d4.44 ± 0.06d12.08 ± 2.07b8.51 ± 1.21c17.81 ± 1.93a5.83 ± 0.16cd3.54 ± 0.47d
      6429050-33-79.914-Carene12.04 ± 0.92cd23.33 ± 0.51a14.74 ± 1.06bc17.81 ± 1.66b2.66 ± 0.40e10.34 ± 1.64d14.13 ± 2.31c
      65483-76-119.10δ-Cadinene12.99 ± 0.13c93.21 ± 2.13a7.78 ± 0.97de6.12 ± 0.41e9.49 ± 0.47d17.99 ± 0.70b6.78 ± 0.55e
      6621391-99-119.51α-Calacorene10.27 ± 0.10b42.73 ± 0.64a6.58 ± 0.53d4.65 ± 0.21e8.12 ± 0.70c10.1 ± 0.44b5.53 ± 0.59de
      67483-77-219.16Calamenene6.57 ± 0.15c61.3 ± 1.03a4.16 ± 0.49de4.66 ± 0.12de5.46 ± 0.70cd16.02 ± 0.98b3.38 ± 0.62e
      685989-27-512.19d-Limonene5.38 ± 0.38cd14.19 ± 0.42a8.26 ± 0.99b8.95 ± 1.46b0.94 ± 0.23e4.99 ± 0.79d7.49 ± 1.14bc
      Terpenoids (13)475.44 ± 24.58c1281.32 ± 15.03a625.07 ± 57.13b709.29 ± 67.59b172.83 ± 17.3d430.14 ± 56.04c620.98 ± 89.21b
      693681-82-19.64(E)-3-Hexen-1-ol acetate125.08 ± 1.45d223.82 ± 15.90cd1030.93 ± 119a690.15 ± 92.67b398.2 ± 79.2c981.62 ± 42.12a871.21 ± 172.5ab
      7061444-38-016.64(Z)-3-Hexenyl (Z)-3-hexenoate10.90 ± 0.08d10.16 ± 0.50d46.93 ± 2.48b48.58 ± 3.24b7.03 ± 0.63d23.17 ± 1.03c54.08 ± 3.55a
      7131501-11-816.57(E)-hex-3-enyl hexanoate5.27 ± 0.10e23.15 ± 0.86d76.15 ± 6.66a26.00 ± 2.08d22.04 ± 1.86d43.61 ± 1.26b36.01 ± 2.91c
      722497-18-99.82(E)-2-Hexenyl acetate0.00 ± 0.00c1.48 ± 0.15bc8.98 ± 1.42a2.34 ± 0.40bc7.07 ± 1.83a0.00 ± 0.00c3.23 ± 0.91b
      7365405-77-822.84(Z)-3-Hexenyl salicylate0.12 ± 0.01d4.60 ± 0.07a4.55 ± 0.53a2.40 ± 0.32c4.28 ± 0.37b3.78 ± 0.16b2.22 ± 0.21d
      7441519-23-712.99(Z)-3-Hexenyl isobutyrate9.20 ± 0.41d20.40 ± 1.44c83.98 ± 8.54a12.03 ± 1.63cd12.81 ± 1.82cd39.75 ± 1.61b34.51 ± 4.53b
      7553398-85-914.82(Z)-3-Hexenyl 2-methylbutyrate0.00 ± 0.00e0.22 ± 0.02d1.23 ± 0.15a0.21 ± 0.02d0.28 ± 0.02d0.87 ± 0.06b0.53 ± 0.08c
      7635852-46-113.90(Z)-3-Hexenyl valerate0.23 ± 0.05e0.61 ± 0.10e5.30 ± 0.89a1.89 ± 0.32cd3.32 ± 0.51b2.15 ± 0.05c1.05 ± 0.13de
      771189-09-915.51Methyl geranate3.73 ± 0.18de28.76 ± 1.08a23.44 ± 2.68b5.40 ± 0.07d3.02 ± 0.09de1.48 ± 0.24e10.69 ± 0.77c
      78150-84-523.07Citronellyl acetate26.23 ± 4.83bc22.86 ± 3.66bc24.37 ± 1.79bc15.38 ± 4.07c50.53 ± 12.99a18.84 ± 7.95bc32.85 ± 6.84b
      792051-49-216.72Hexyl hexanoate0.00 ± 0.00e2.77 ± 0.15c9.44 ± 1.23a1.56 ± 0.19cd4.97 ± 0.39b0.97 ± 0.05de1.18 ± 0.08de
      80142-92-79.78Hexyl acetate0.00 ± 0.00d1.57 ± 0.27cd8.85 ± 1.48a2.79 ± 0.49bc4.39 ± 1.06b2.59 ± 0.17bc1.92 ± 0.54c
      81120-51-422.56Benzyl benzoate2.17 ± 0.12cd4.02 ± 0.53a2.95 ± 0.53bc3.00 ± 0.45bc3.85 ± 0.42b1.96 ± 0.18d2.52 ± 0.27cd
      82110-27-022.96Isopropyl myristate0.06 ± 0.01e2.55 ± 0.18a1.35 ± 0.12bc1.34 ± 0.07bc1.14 ± 0.07cd1.03 ± 0.03d1.44 ± 0.06b
      83606-45-115.77Methyl 2-methoxybenzoate7.30 ± 0.20a0.56 ± 0.03e3.00 ± 0.26b2.02 ± 0.08c0.42 ± 0.03e1.01 ± 0.02d0.97 ± 0.11d
      84102-16-914.94Benzyl phenylacetate0.97 ± 0.07b1.64 ± 0.04a1.07 ± 0.19b0.73 ± 0.07c0.38 ± 0.02d0.49 ± 0.02d0.74 ± 0.05c
      857011-83-816.72Dihydrojasmone lactone0.00 ± 0.00e3.14 ± 0.11c11.60 ± 1.36a1.65 ± 0.18d5.14 ± 0.36b1.36 ± 0.02d1.52 ± 0.11d
      8625524-95-216.57Jasmine lactone5.34 ± 0.11e23.23 ± 0.92d76.66 ± 6.57a26.21 ± 1.94d22.16 ± 1.84d43.74 ± 1.33b36.1 ± 2.91c
      871211-29-623.24Methyl jasmonate12.81 ± 2.06b13.26 ± 2.07b12.50 ± 1.11b8.73 ± 1.89b27.01 ± 4.9a10.85 ± 4.47b15.19 ± 1.67b
      88119-36-813.19Methyl salicylate294.81 ± 22.12d2,085.7 ± 131.83a307.14 ± 36.23d498.63 ± 15.53c764.22 ± 42.52b516.16 ± 30.26c483.65 ± 46.37c
      Esters (20)504.25 ± 27.11d2,474.51 ± 157.38a1,740.43 ± 183.45b1,351.05 ± 114.32c1,342.27 ± 103.23c1,695.42 ± 61.37b1,591.61 ± 204.25bc
      All data are shown as the mean ± standard deviation (SD). Significant differences among various groups are represented by different letters (p < 0.05).. RT: retention time.

      The phenotypes of seven tea cultivars are shown in Fig. 1a. The PCA score plot of the main chemical components indicated that the first two principal components explained 27.5% and 21.3% of the total variance, respectively, and the cumulative variance contribution reached 49.8% (Fig. 1b), which indicated that PC1 and PC2 were selected to analyze the samples with good reliability. The seven tea cultivars showed different distribution characteristics in the PCA score plot: JX, BHZ, and TGY were far away from other cultivars; FDDB and FJSX were relatively close; and LJ43 and SCZ were relatively close.

      Figure 1. 

      Multivariate statistical analysis of volatile components of seven tea cultivars. (a) Phenotypes of one bud and two leaves and their suitability. (b) PCA principal component analysis. (c) Types and relative contents of volatile components. (d) Proportions of volatile components.

      The comparative analysis identified differences in the relative content of volatiles in the seven tea cultivars (Fig. 1c, d). The volatile aroma substances with the highest contents in TGY were mainly phenols. In particular, eugenol had the highest levels of contents in TGY as compared to other cultivars. Among these compounds, alcohols and esters were present in the greatest numbers, indicating major contributions to aroma. The content of alcohols and esters in JX were higher than in other cultivar aroma types, nerol, geraniol, myrcene, (E)-β-ocimene, δ-cadinene and d-limonene had higher concentrations in JX than other cultivars. Compared with other cultivars, these aroma categories varied in FJSX was not abundant. Interestingly, Jasmine lactone had the highest concentrations in FJSX. The representative green tea cultivars showed higher contents of alcohol and aldehyde. In the alcohol group, linalool had the highest concentrations in FDDB than other cultivars, (E)-nerolidol and (Z)-3-hexenol had the highest concentrations in SCZ than other cultivars. The content of aldehydes in FDDB and SCZ than other cultivars, for example, phenylacetaldehyde had higher concentrations than other cultivars. In the aldehyde group, hexanal and butanal had the highest concentrations in LJ43. It is worth noting that indole had the highest concentrations in BHZ than other cultivars.

      Analysis of differential volatile components of seven tea cultivars

    • A total of 54 significantly changed metabolites (SCMs) were identified in seven tea cultivars (Fig. 2). Specifically, the study found that the proportion of up-regulated terpenoids was highest in JX, while the proportion of down-regulated terpenoids was highest in BHZ. Among the esters, the proportion of up-regulated compounds was highest in FJSX, while the proportion of down-regulated compounds was highest in TGY. Additionally, the content of three phenolic compounds was significantly up-regulated in TGY, and the content of alcohol compounds was significantly up-regulated in FDDB.

      Figure 2. 

      Analysis of differential volatile components of seven tea cultivars.

      Furthermore, the study conducted a more detailed analysis of the compounds with significantly increased content in each tea variety. In TGY and JX, the content of β-caryophyllene and α-caryophyllene was significantly higher than in other cultivars. The highest proportion of eight ester compounds was found in FJSX. Phenylethyl alcohol and phenylacetaldehyde levels showed significant variation in seven cultivars, with the highest levels found in FDDB. In SCZ, LJ43, and BHZ, the content of (Z)-3-hexenol was significantly up-regulated. In SCZ and BHZ, the levels of (E)-nerolidol and jasmone were significantly up-regulated. Finally, the content of indole was significantly accumulated in JX and BHZ, while the levels of α-farnesene and methyl jasmonate were significantly higher in BHZ than in other cultivars.

      OAV value of differential volatile components in seven tea cultivars

    • The odor activity values (OAVs) of the identified volatiles are shown in Table 2. A total of 26 volatiles were determined to have OAV > 1 in tea samples, of which two volatiles had OAVs ≥ 1000. The OAVs of β-damascenone (OAV: 503.45-9026.77), β-ionone (OAV: 1245.71-1631.16), geraniol (OAV: 31.96-680.45), linalool (OAV: 79.68-410.85) and phenylacetaldehyde (OAV: 4.74-186.67) were higher than those of other compounds, indicating that they played significant roles in the aroma of the seven tea cultivars.

      Table 2.  OAV values of differential volatile components in seven tea cultivars.

      Volatile
      components      		Aroma
      characteristics      		Aroma thresholds[3,27−30]
      (μg/kg)      		Relative OAV value
      TGYJXFJSXFDDBBHZLJ43SCZ
      GeraniolRosy, sweet7.5181.19680.45402.37343.1931.96176.24334.77
      LinaloolFloral, fruity6168.78209.73184.39410.8579.68202.16234.71
      (E)-NerolidolFloral, citrus153.653.654.842.277.164.477.67
      NerolRosy, orange490.461.471.011.240.230.731.23
      (Z)-3-HexenolFresh, grassy1100.320.310.721.620.671.552.08
      Phenylethyl alcoholFloral, rosy452.112.550.3416.449.217.5215.32
      (Z)-Linalool oxide (furanoid)Sweet, floral1900.140.520.231.350.451.070.69
      (E)-Linalool oxide (furanoid)Sweet, floral1900.431.660.433.511.002.151.57
      NeralSweet, fruity530.481.771.141.240.100.711.13
      DecanalSweet, citrus0.15.906.386.938.208.6011.898.13
      HeptanalFatty, citrus1014.310.220.220.160.120.110.09
      BenzaldehydeAlmond, nutty33.272.732.062.913.003.694.29
      PhenylacetaldehydeWoody, sweet424.8530.284.72186.67108.7589.10175.02
      HexanalFresh, fruity, fatty4.51.743.432.774.146.136.554.99
      ValeraldehydeAlmond, malty121.201.741.122.022.792.593.35
      IsovaleraldehydeFruity412.225.288.309.258.5911.419.79
      α-IononeViolet, woody0.430.1358.2036.7544.536.5025.8535.27
      β-IononeViolet, floral0.071,245.711,299.601,327.561,478.321,589.211,549.001,631.16
      JasmoneJasmine70.950.680.610.741.361.121.78
      β-DamascenoneRosy0.002503.459,026.777,127.533,791.88911.923,053.124,509.23
      IndoleFloral40–0.91–0.291.060.190.62
      (E)-β-OcimeneFloral, grassy343.167.764.355.120.702.924.57
      MyrceneFruity, balsamic1516.1044.6124.9928.412.9415.3724.26
      (E)-3-Hexen-1-ol acetateFruity, grassy314.037.2233.2622.2612.8531.6728.10
      Methyl salicylateHerbal, minty407.3752.147.6812.4719.1112.9012.09
      Methyl jasmonateJasmine34.274.424.172.919.003.625.06
      '–' indicates that the OAV value cannot be calculated.

      There were 21 volatiles with 1 ≤ OAV ≤ 100 in tea samples, of which eight volatiles (phenylethyl alcohol, heptanal, decanal, isovaleraldehyde, α-ionone, myrcene, (E)-3-hexen-1-ol acetate, methyl salicylate) had OAVs ≥ 10. Importantly, we found that heptanal was abundant in TGY with OAV > 1. In total, 13 volatile compounds had OAVs > 1 in tea samples, of which (Z)-3-hexenol (SCZ, FDDB, LJ43), (Z)-linalool oxide (FDDB, LJ43), and jasmone (SCZ, BHZ, LJ43) had OAVs ≥ 1. In addition, indole was determined to have OAVs > 1 in BHZ.

      Differentially expressed genes (DEGs) related to three volatile synthesis pathways

    • Terpenoids, unsaturated aliphatic compounds and aromatic compounds are the main aroma components of tea. To explain the mechanism of up- or downregulation of SCMs in seven tea cultivars at the molecular level, we identified DEGs in relevant biosynthetic pathways.

      For the terpenoid volatiles synthesis pathway, a total of 20 DEGs and 40 TPS genes were involved in Mevalonate pathway (MVA) and 2-methyl-D-erythritol-4-phosphate pathway (MEP), and six DEGs were involved in the carotenoid metabolic pathway (Fig. 3). Among them, HMGR (CsTGY05G0000313) and HMGS (CsTGY01G0002862) were significantly upregulated 5.55- and 5.46-fold in JX and SCZ, respectively. CsHMGR (3-hydroxy-3-methylglutaryl coenzyme A reductase) and HMGS (3-hydroxy-3-methylglutaryl-CoA synthase) are key rate-limiting enzymes in the MVA of the terpene derivative pathway[31]. LIS/NES (CsTGY08G0001704) and NES/GIS (CsTGY08G0001826) were significantly upregulated 3.09- and 4.7-fold in FDDB and BHZ, respectively. LIS/NES (linalool/nerolidol synthase) and NES/GIS (nerolidol/geranyl linalool synthase) are the key enzyme in the biosynthesis of linalool and nerolidol. (E)-nerolidol has clean and floral aromas, linalool has the fragrance of rose and fruit, and its oxidized products have woody, floral, and camphor odours, which are the main aroma components of tea[3].

      Figure 3. 

      Expression profiles of genes related to the terpene synthesis pathway. (a) Heatmap of the expression pattern of CsTPS genes. (b) Biosynthetic pathway of terpenes and expression patterns of related DEGs. (c) Metabolic pathway of carotenoids and expression patterns of related DEGs.

      For the α-linolenic acid metabolism pathway, 29 DEGs were involved in the α-linolenic acid metabolism pathway (Fig. 4), mainly including acyl-CoA oxidase (ACX), OPC-8:0 CoA ligase (OPCL), allene oxide cyclase (AOC), lipoxygenase (LOX), and alcohol dehydrogenase (ADH). Among these genes, ACX (CsTGY04G0002749) was significantly upregulated 3.76-fold in FDDB. OPCL (CsTGY07G0001858) was only expressed in SCZ. AOC (CsTGY01G0003195) was significantly downregulated 2.35-fold in FJSX, and ADH (CsTGY09G0001886) showed higher expression in TGY and FJSX than in the other tea cultivars. ADH (alcohol dehydrogenase) is a key enzyme responsible for the biosynthesis of the key volatile C6-compounds in green tea leaves, which are important precursors of tea aroma[32]. In particular, (Z)-3-hexenol has grassy odor, (E)-3-hexen-1-ol acetate has grassy and fruity aroma, among which the former is considered to be the main source of green tea aroma[33].

      Figure 4. 

      Expression profiles of genes related to the α-linolenic acid metabolism pathway.

      To deeply investigate the mechanisms that regulate the biosynthesis of phenylpropanoid/benzenoids in tea plants, we thoroughly studied the DEGs in the pathways (Fig. 5). In total, 11 DEGs were involved in the phenylpropanoid/benzenoid synthesis pathway, mainly including catechol-O-methyltransferase (COMT), caffeoyl-coenzyme A O-methyltransferase (CCoCOMT), alcohol dehydrogenase (CAD), eugenol synthase (EGS), and benzoic acid carboxyl methyltransferases (BAMT). Among these DEGs, COMT (CsTGY08G0002131), CCoCOMT (CsTGY06G0000958), and CAD (CsTGY04G0002121) showed the highest expression in TGY and were significantly upregulated 3.77-, 7.75-, and 4.63-fold, respectively. COMT, CCoCOMT, and CAD are involved in the biosynthesis of eugenol, which is the main aromatic component of cloves and orchids, with clove aroma[34]. In addition, EGS (CsTGY01G0002412) was significantly upregulated 3.93-fold in JX; BAMT (CsTGY03G0002449, CsTGY03G0002452) was significantly upregulated 3.99- and 52.52-fold in BHZ.

      Figure 5. 

      Expression profiles of genes related to the phenylpropanoid/benzenoid synthesis pathway.

      Coexpression network related to key aroma compound formation

    • To understand the gene regulation mechanism of aroma biosynthesis, we correlated 11,222 genes and 20 SCMs were used for WGCNA. After merging similar modules, 14 modules were generated, which comprised 95 to 2,503 genes. Figure 6 showed the correlation between 14 modules and 20 characteristic volatiles. Modules with larger correlation coefficients and smaller p-values are highly correlated phenotypes (r ≥ 0.7, p-value < 0.05). Among these volatiles, the brown module was significantly correlated with (Z)-3-hexenol and (E)-3-hexen-1-ol acetate (r = 0.825, 0.755); the blue module was significantly correlated with β-caryophyllene (r = 0.81); the pink module was significantly correlated with linalool (r = 0.793). The results indicated that these modules play an important role in aroma biosynthesis in fresh leaves of tea plants.

      Figure 6. 

      Coexpression network related to key aroma compound formation. Matrix of module-metabolite associations. The abscissa represents different phenotypes, and the ordinate represents different modules. The number of genes per module is shown in the left box. Correlation coefficients and p-values between modules and metabolites are shown at the row-column intersection. Red means the module has a greater correlation with the phenotype, and blue means the module has a lower correlation with the phenotype.

      To reveal the complex transcriptional regulatory network of aroma formation, genes from four modules were analysed, resulting in the identification of CsHMGR (CsTGY04G0000045), CsDXS (CsTGY02G0002953), and CsTPS (CsTGY05G0001285) genes in the blue module, two CsLIS/NES genes (CsTGY08G0001704, CsTGY08G0000359) in the pink module, and CsMVK (CsTGY05G0001238) and CsADH (CsTGY09G0001879) genes in the brown module. When r > 0.8, there may be a strong relationship between the two nodes. Therefore, transcription factors (TFs) were screened from blue, pink and brown modules. Transcriptional regulatory networks were constructed with the above genes (Fig. 7).

      Figure 7. 

      Coexpression network diagram of candidate genes. When the correlation coefficient (r) is greater than 0.8, we believe that there is a regulatory relationship between the candidate genes and TFs. Nodes with the same colour indicate that the correlation coefficient between candidate genes and TFs is greater than 0.8, and the line colours between nodes indicate the strength of the correlation.

      In the blue module, there were 23, 9, and 19 TFs that were significantly correlated with CsHMGR, CsDXS, and CsTPS, respectively. These TFs may be directly or indirectly involved in gene expression and β-caryophyllene biosynthesis in fresh leaves. MYB (CsTGY01G0002788, CsTGY02G0001042, CsTGY04G0002761, CsTGY06G0001034, CsTGY14G0000893), bHLH (CsTGY03G0003180), and NAC (CsTGY07G0002415) showed the strongest relationship to CsHMGR (r > 0.9); TCP (CsTGY04G0003490) and HB-other (CsTGY07G0002073) showed the strongest relationship to CsDXS (r > 0.9); TCP (CsTGY04G0003490) and HB-other (CsTGY07G0002073) showed the strongest relationship to CsDXS (r > 0.9); NAC (CsTGY13G0001061), HB-other (CsTGY01G0003094), and bHLH (CsTGY12G0001520) showed the strongest relationship to CsTPS (r > 0.9).

      In the pink module, three and eight TFs were significantly correlated with CsLIS/NES, respectively. Among them, bHLH (CsTGY06G0000306) and WRKY (CsTGY10G0000752) showed the strongest relationship to CsLIS/NES1 (r > 0.9); MYB (CsTGY15G0001797), ERF (CsTGY12G0001243), bHLH (CsTGY12G0001940) and bZIP (CsTGY09G0000052) showed a significant positive correlation with CsLIS/NES2 (r > 0.8).

      The brown module identified seven TFs, and these TFs may be involved in the regulation of CsMVK and CsADH genes. B3 (CsTGY07G0001850) and NAC (CsTGY07G0000158) showed a significant positive correlation with CsMVK (r > 0.8); MYB (CsTGY01G0001203, CsTGY04G0001918, CsTGY06G0002545), C3H (CsTGY15G0000151) and GRAS (CsTGY14G0002018) showed a significant correlation with CsADH (r > 0.8). These results suggested that the TFs above may be involved in regulating the characteristic volatile components and their key genes in fresh leaves of tea cultivars.

      Analysis of DEG expression level by PCR

    • To verify the accuracy of the transcriptome data, the transcript abundances of eight selected DEGs were analysed by qRT-PCR. In total, four DEGs in the terpene synthesis pathway, two DEGs, and two TFs in the LOX pathway were identified (Fig. 8). The relative expression of qRT-PCR was consistent with the trend of RNA-Seq, indicating that the transcriptome sequencing results could be reliable.

      Figure 8. 

      Verification of the expression levels of eight differentially expressed genes.

    Discussion

    • Tea plant cultivars possess distinct genetic and biochemical characteristics, which largely determine their suitable tea species and quality[35]. In previous studies[18], we have found that the contents of catechin and purine alkaloids in TGY, JX, and FJSX were higher, the contents of sweet amino acids and sugars in FDDB were higher, and the contents of free amino acids and nucleotides in suitable green tea cultivars were higher. In addition to non-volatile metabolites, volatiles are also particularly important for the formation of tea quality. Aromatic substances in fresh leaves are the material basis for the formation of tea aroma[36]. Tea cultivars play an important role in the chemical compositions of fresh tea leaves and the enzymatic activities of aroma volatile-related enzymes[37]. Therefore, we further analyzed the volatile components of these seven tea cultivars to explore the reasons for their unique flavor from the raw materials of fresh leaves.

      Each tea cultivar possesses its own phenotype and characteristic metabolites, some suitable for processing green tea, white, black, or oolong tea. The abundance of terpenes in fresh leaves plays an important role in the aroma quality of tea, which usually has an attractive floral and fruity aroma[38]. High ratio of terpenoid volatiles to green leaf volatiles could be regarded as a good indicator in screening cultivar for suitably producing high quality oolong tea[5]. The terpenoids in TGY and JX accounted for about 72% of the total contents, we speculated that this may be one of the reasons for their suitable preparation of oolong tea. Furthermore, the eugenol[34] and heptanal[28] may contribute to the fruity and flower characteristics of TGY, and methyl salicylate[3], (E)-β-ocimene[3], and geraniol[27] may be beneficial to JX tea aroma. During processing, Zeng et al.[39] found the GLVs and monoterpenes have relatively large changes in JX, the content of homoterpenes changed sharply in TGY. The differences in in gene expression regulation may all affect the production and concentration of terpenes in plants[40]. The content of esters in the fresh leaves of tea plants was only lower than that of alcohols, among which jasmine lactone is the key volatile component that gives oolong tea its fatty, dairy and floral characteristics[3], which may play an important role in the aroma formation of FJSX. Tea aroma intensity was significantly related to the contents of esters in tea leaves, and the higher the content, the better the quality. The triploid tea plants increase the gene dose due to the doubling of chromosomes, and the amount of transcription and expression products will inevitably change accordingly[41]. The doubling of chromosomes first leads to changes in the genomic structure, resulting in the re-regulation of gene expression and changes in gene expression levels. The results may be related to the fact that FJSX is the triploid tea resource.

      Phenylethyl alcohol, phenylacetaldehyde, linalool, and its oxidized products were the main volatiles of white tea[42], we speculated that the significant accumulation of linalool and linalool oxide content in FDDB may contribute to the clear and fresh characteristics of the white tea. Aromatic alcohols with floral and fruit aromas were not abundant in the fresh leaves of tea plants[1], among which phenylethyl alcohol and phenylacetaldehyde may be the main material basis for white tea and green tea to have mellow aroma quality[6]. Green tea has a variety of flavour characteristics, such as scent types of floral, fruity, nutty, chestnut-like fragrances, and so on[43]. (Z)-3-hexenol could play a determining role in the 'raw grass' odour of finished green tea due to its overly strong and sharp green aroma[44], and (E)-3-Hexen-1-ol acetate has a grassy and fruity flavor[30]. The contents of these aroma components were higher in cultivars suitable for green tea, which may be closely related to the aroma formation.

      The biosynthesis of terpenes with floral and fruit aromas is catalyzed by TPS enzymes via either the MVA or MEP pathway[45]. Our previous study[9, 30] showed that CsTPS have different functions in the synthesis of terpenoids. Studies have found that CsLIS might be involved in the regulation of linalool accumulation in the tea manufacturing process[30], and CsNES were highly expressed in oolong tea during tea turning (E)-nerolidol accumulation[46]. In this study, CsLIS/NES (CsTGY08G0001704) might be the key gene affecting the accumulation of linalool in FDDB fresh leaves, and CsNES/GIS (CsTGY08G0001826) were significantly upregulated in BHZ which might be related to the higher content of (E)-nerolidol. Volatile aliphatic components were biosynthesized from linoleic acid and linolenic acid via the lipoxygenase (LOX) pathway[37]. Studies have confirmed that COMT, CCoCOMT, CCR, and CAD are involved in the biosynthesis of eugenol[47−50]. Our results showed that the expression levels of CsCOMT (CsTGY08G0002131), CsCCoCOMT (CsTGY06G0000958), and CsCAD (CsTGY04G0002121) may be closely related to the biosynthesis of eugenol in TGY.

      AP2/ERF, bHLH, WRKY, MYB, NAC, and bZIP were the common TF families that regulate terpenoid synthesis[51]. Based on WGCNA study, we speculated that bHLH, MYB, WRKY, and NAC TFs may play an important role in inducing the synthesis of β-caryophyllene by regulating CsHMGR (CsTGY04G0000045), CsDXS (CsTGY02G0002953), and CsTPS (CsTGY05G0001285). In Arabidopsis thaliana, the induction of TPS21 and TPS11 results in increased emission of sesquiterpenes, especially (E)-β-caryophyllene[52]. Overexpression of CpMYC2 and CpbHLH13 in Arabidopsis thaliana and tobacco can promote the synthesis of linalool and β-caryophyllene[53, 54]. Our study also identified that CsbHLH (CsTGY12G0001520) was annotated as MYC2, which may related to β-caryophyllene accumulation in fresh leaves by regulating the expression of the CsTPS (CsTGY05G0001285). In grape berries, VtNAC, VtC2C2-GATA, and VtbHLH were involved in the synthesis of linalool by regulating TPS genes[7]. TFs such as bHLH, WRKY, NAC, and ERF were directly involved in the regulation of linalool synthesis by binding with promoters of CsLIN[30]. Transcription factors play an essential regulatory role in the growth and development of tea plants and complex with other transcription factors to regulate plant secondary metabolism[55]. In Freesia hybrida and Arabidopsis thaliana, AtMYB21 and AtMYC2 were confirmed to participate in linalool synthesis by interacting with each other to form MYB-bHLH complex to control the expression of linalool synthase genes[8]. In this study, bHLH, WRKY, ERF, and MYB were involved in the biosynthesis of linalool in fresh leaves by regulating CsLIS/NES1 (CsTGY08G0000359) and CsLIS/NES2 (CsTGY08G0001704), especially ERF (CsTGY02G0001232) may be the key transcription factor affecting linalool synthesis. In tea plant, CsMYB is a key gene in lipid metabolism, and it mainly affects lipid metabolism by regulating CsADH to participate in aroma biosynthesis[25]. And CsMYB is involved in the biosynthesis of fatty acid derivatives by regulating the LOX pathway in green tea[56]. In the present study, we speculated that CsMYB (CsTGY01G0001203, CsTGY04G0001918, CsTGY06G0002545) may play an important role in regulating the expression of CsADH (CsTGY09G0001879) and the accumulation of (Z)-3-hexenol and (E)-3-hexene-1-ol acetate in fresh leaves.

    Conclusions

    • In this study, we conducted a comprehensive metabolomic and transcriptomic analysis of seven tea plant cultivars to investigate the cultivar characteristic volatile components of different tea plants and their possible molecular mechanisms leading to volatile component accumulation. Overall, terpenes accounted for a large proportion of the fresh leaves of oolong tea cultivars. The aroma compositions of white and green tea cultivars were similar, and the contents of (Z)-3-hexenol, phenylethyl alcohol, phenylacetaldehyde, linalool, and its oxides were higher. The accumulation of volatile components is not only controlled by the expression of structural genes but also involved in the regulation of many transcription factors. Our study revealed the characteristic volatile components and their key regulatory genes of seven tea cultivars, which will provide a theoretical basis for breeding and suitability research of tea cultivars.

      Acknowledgments

      • This research was funded by the Major Special Project of Scientific and Technological Innovation on Anxi Tea (Grant No. AX2021001), Fujian Agriculture and Forestry University Construction Project for Technological Innovation and Service System of Tea Industry Chain (K1520005A01).

      Conflict of interest

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

      • # These authors contributed equally: Ting Gao, Shuxian Shao

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      • Copyright: © 2023 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 (8)  Table (2) References (56)
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    Gao T, Shao S, Hou B, Hong Y, Ren W, et al. 2023. Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China. Beverage Plant Research 3:17 doi: 10.48130/BPR-2023-0017
    Gao T, Shao S, Hou B, Hong Y, Ren W, et al. 2023. Characteristic volatile components and transcriptional regulation of seven major tea cultivars (Camellia sinensis) in China. Beverage Plant Research 3:17 doi: 10.48130/BPR-2023-0017
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  • Introduction
  • Materials and methods

  • Tea plant materials

  • Analysis of volatile metabolites

  • Detection of volatile compounds

  • Transcriptome-based analysis of aroma component-related pathways and differentially expressed genes

  • Gene coexpression analysis

  • Quantitative real-time PCR verification

  • Results

  • The volatile components of seven tea cultivars

  • Analysis of differential volatile components of seven tea cultivars

  • OAV value of differential volatile components in seven tea cultivars

  • Differentially expressed genes (DEGs) related to three volatile synthesis pathways

  • Coexpression network related to key aroma compound formation

  • Analysis of DEG expression level by PCR

  • Discussion
  • Conclusions
  • Acknowledgments
  • Conflict of interest
  • Supplementary information
  • Rights and permissions
  • About this article
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