- Record: found
- Abstract: found
- Article: found
Metabolome and Transcriptome Analysis Reveals Putative Genes Involved in Anthocyanin Accumulation and Coloration in White and Pink Tea ( Camellia sinensis) Flower
research-article
Author(s):
Caibi Zhou
1
,
2 ,
Xin Mei
3 ,
Dylan O’Neill Rothenberg
2 ,
Zaibo Yang
1 ,
Wenting Zhang
2 ,
Shihua Wan
2 ,
Haijun Yang
2 ,
Lingyun Zhang
2
,
*
Publication date (Electronic):
02 January 2020
Journal:
Molecules
Publisher:
MDPI
Read this article at
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
A variant of tea tree ( Camellia sinensis (L.)) with purple buds and leaves and pink flowers can be used as a unique ornamental plant. However, the mechanism of flower coloration remains unclear. To elucidate the molecular mechanism of coloration, as well as anthocyanin accumulation in white and pink tea flowers, metabolite profiling and transcriptome sequencing was analyzed in various tea flower developmental stages. Results of metabolomics analysis revealed that three specific anthocyanin substances could be identified, i.e., cyanidin O-syringic acid, petunidin 3- O-glucoside, and pelargonidin 3- O-β- d-glucoside, which only accumulated in pink tea flowers, and were not able to be detected in white flowers. RNA-seq and weighted gene co-expression network analysis revealed eight highly expressed structural genes involved in anthocyanin biosynthetic pathway, and particularly, different expression patterns of flavonol synthase and dihydroflavonol-4-reductase genes were observed. We deduced that the disequilibrium of expression levels in flavonol synthases and dihydroflavonol-4-reductases resulted in different levels of anthocyanin accumulation and coloration in white and pink tea flowers. Results of qRT-PCR performed for 9 key genes suggested that the expression profiles of differentially expressed genes were generally consistent with the results of high-throughput sequencing. These findings provide insight into anthocyanin accumulation and coloration mechanisms during tea flower development, which will contribute to the breeding of pink-flowered and anthocyanin-rich tea cultivars.
Related collections
Most cited references52
- Record: found
- Abstract: found
- Article: not found
Cytoscape: software for visualization and analysis of biological networks.
Michael Köhl, Sebastian Wiese, Bettina Warscheid (2011)
Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.
- Record: found
- Abstract: found
- Article: found
Transcriptome sequencing and metabolite analysis reveals the role of delphinidin metabolism in flower colour in grape hyacinth
Introduction Grape hyacinth (Muscari) is an important ornamental bulbous plant with a unique flower shape, extraordinary blue colour, and sweet fragrance (Qi et al., 2013). These quality traits are largely determined by the metabolic composition of the flower. For example, anthocyanins are the principal flower pigments in Muscari flowers (Mori et al., 2002). It is reported that the varying shades found in the blue flowers are attributable to delphinidin (Del), while the reddish hues are attributable to cyanidin (Cy) (Qi et al., 2013). Anthocyanins are among the most studied and best understood compounds in plant science, and their metabolic pathway has been extensively described (Grotewold, 2006; Tanaka et al., 2008). Nevertheless, the mechanisms that control anthocyanin catabolism in different plant species are far from conclusive. It is reasonable to expect that such loss-of-colour adaptations are relatively unconstrained because they can be achieved in many ways (Clark and Verwoerd, 2011). The numerous diverse metabolic pathways by which plant compounds can be produced makes it more difficult to clarify this matter. The increased ease and efficiency of RNA sequencing (RNA-Seq) tools will facilitate the study of the mechanisms underlying metabolite variation. However, it is still hard to imagine a direct correlation between the transcript abundance and the level of respective metabolite. After all, there are always too many variable factors to reach a clear conclusion. On the basis of metabolite analysis, a stringent logical filter for high-throughput approaches could be set up and used to identify the relevant factors and to circumvent the ambiguities resulting from the transcriptome comparison between different varieties. By choosing an integrative approach, where not only are transcript levels investigated, but also the metabolic products are compared, it is possible to gain an insight into metabolic flows, which would not be possible from transcript analysis alone. Thus the natural variation in blue M. armeniacum flower (the white form of M. armeniacum) provides opportunities for insight into complex metabolic networks and certain biochemical traits, especially colour. In the present study, the first RNA-Seq project for M. armeniacum and its white variant was performed using the Illumina sequencing technique. Through a combination of chemical analysis with bioinformatics, the major metabolic pathways related to Muscari flower pigmentation were deduced and the candidate genes targeting the loss of pigmentation in the plants were examined. Materials and methods Plant material The little florets just before blooming of M. armeniacum and its white form, M. armeniacum f. album were collected at 08:00h on 10 April 2012 at Xi’an Botanical Garden, Shaanxi, PR China (Fig. 1A–D). All samples were immediately frozen in liquid nitrogen and stored at –80 °C for RNA extraction and flavonoid analysis. Fig. 1. A diagram of the putative anthocyanin metabolic process in blue or white M. armeniacum flowers. (A) Mature inflorescence of M. armeniacum. Arrows represent small flower buds just before bloom. (B) Flower bud of M. armeniacum just before bloom used in deep sequencing. (C) Mature inflorescence of M. armeniacum f. album. (D) Flower bud of M. armeniacum f. album just before bloom used in deep sequencing. The scale bar=2mm in A and C, and 1mm in B and D. (E) The putative anthocyanin metabolic process in blue M. armeniacum flowers. (F) The putative anthocyanin metabolic process in white M. armeniacum flowers. (G) Flavonoid composition obtained by HPLC from blue and white flowers of M. armeniacum. ANR, anthocyanidin reductase; ANS, anthocyanidin synthase; Ca, catechin; CHI, chalcone isomerase; CHS, chalcone synthase; Cy, cyanidin; Del, delphinidin; DFR, dihydroflavonol 4-reductase; DHM, dihydromyricetin; Ep, epicatechin; Er, eriodictyol; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F3′5′H, flavonoid 3′5′-hydroxylase; FLS, flavonol synthase; Km, kaempferol; LAR, leucoanthocyanidin reductase; UFGT, anthocyanidin 3-O-glucosyltransferase. (This figure is available in colour at JXB online.) Measurement of flower flavonoids The anthocyanins were determined using high-performance liquid chromatography (HPLC) as previously described (Qi et al., 2013). For extraction of other flavonoids, freeze-dried flowers were finely ground and 50mg was extracted in 500 μl of MeOH for 48h at 4 °C in darkness. After samples were centrifuged, the supernatants were transferred to fresh tubes and the pellet was resuspended and incubated in 500 μl of 1% MeOH at 4 °C for 24h, and then the supernatant was combined for further HPLC analysis. HPLC was performed as previously described (Qi et al., 2013). Cyanidin, cyanidin-galactoside, dihydroquercetin, dihydrokaempferol, (+)-catechin, (–)-epicatechin, luteolin, naringenin, and quercetin were obtained from Sigma-Aldrich China (Shanghai). Standards of afzelechin, (–)-epiafzelechin, (+)-gallocatechin, and (–)-epigallocatechin were purchased from BioBioPha (Yunnan, China). The delphinidin chloride (ChromaDex, Santa Ana, CA, USA), petunidin chloride (ChromaDex), and other flavonoids such as dihydromyricetin (YiFang S&T, Tianjin, China) equivalents were used as standards for quantification. Mean values and SDs were obtained from three biological replicates. RNA extraction, library construction, and RNA-Seq Total RNA of each sample was isolated using a Quick RNA isolation kit (Bioteke Corporation, Beijing, China) and then characterized on a 1% agarose gel and examined with a NanoDrop ND1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The RIN (RNA integrity number) values (>8.0) of these samples were assessed using an Agilent 2100 Bioanalyzer (Santa Clara, CA, USA). The construction of the libraries and the RNA-Seq were performed by the Biomarker Biotechnology Corporation (Beijing, China). mRNA was enriched and purified with oligo(dT)-rich magnetic beads and then broken into short fragments. Taking these cleaved mRNA fragments as templates, first- and second-strand cDNA were synthesized. The resulting cDNAs were then subjected to end-repair and phosphorylation using T4 DNA polymerase and Klenow DNA polymerase. After that, an ‘A’ base was inserted as an overhang at the 3′ ends of the repaired cDNA fragments and Illumina paired-end solexa adaptors were subsequently ligated to these cDNA fragments to distinguish the different sequencing samples. To select a size range of templates for downstream enrichment, the products of the ligation reaction were purified and selected on a 2% agarose gel. Next, PCR amplification was performed to enrich the purified cDNA template. Finally, the four libraries were sequenced using an Illumina HiSeq™ 2000. De novo transcriptome assembly and annotation After removing those reads with only adaptor and unknown nucleotides >5%, or those that were of low quality, the clean reads were filtered from the raw reads. The clean reads were then assembled de novo using the Trinity platform (http://trinityrnaseq.sourceforge.net/) with the parameters of ‘K-mer=25, group pairs distance=300’ (Grabherr et al., 2011). For each library, short reads were first assembled into longer contigs based on their overlap regions. Then different contigs from another transcript and their distance were further recognized by mapping clean reads back to the corresponding contigs based on their paired-end information, and thus the sequence of the transcripts was produced. Finally, the potential transcript sequences were clustered using the TGI Clustering tool to obtain uni-transcripts (Pertea et al., 2003). Uni-transcripts were aligned to a series of protein databases using BLASTx (E-value ≤10–5), including the NCBI non-redundant (Nr), the Swiss-Prot, the Trembl, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/kegg2.html), and gene ontology (http://wego.genomics.org.cn/cgi-bin/wego/index.pl) databases. To determine the gene coverage, the reference sequences for all three colour-related pathways were downloaded from the public databases (Supplementary Fig. S1, Supplementary Table S1 available at JXB online). All isoforms of all colour-related genes present in the databases examined were aligned against corresponding reference sequences using BLASTx. The deduced amino acid sequences of uni-transcripts were required to be longer than 70% of the corresponding sequences. If a uni-transcript met the criteria, it was assumed to contain a near full-length contig. If not, targeted assembly was performed to obtain even greater coverage of the respective genes. All reads in the databases examined were mapped to the reference sequences and the mapped reads were then assembled using clustering and CAP3 assembly (http://compbio.dfci.harvard.edu/tgi/software/). Expression annotation To evaluate the depth of coverage, all usable reads were realigned to each uni-transcript using SOAPaligner (http://soap.genomics.org.cn/soapaligner.html), then normalized into RPKM values (reads per kb per million reads; Mortazavi et al., 2008). After that, uni-transcript abundance differences between the samples were calculated based on the ratio of the RPKM values, and the false discovery rate (FDR) control method was used to identify the threshold of the P-value in multiple tests in order to compute the significance of the differences in transcript abundance (Benjamini and Yekutieli, 2003). Here, only uni-transcripts with an absolute value of log2 ratio ≥2 and an FDR significance score 88% of genes in the flavonoid biosynthesis pathway (Supplementary Fig. S1). However, only a small percentage of genes in the other two pathways was found (Supplementary Fig. S1). Possible reasons for this might be the metabolite diversification in different species. In support of this, no sequences for methoxylation genes involved in anthocyanin modification were assembled, which was consistent with the absence of methylated anthocyanin in the flowers of M. armeniacum (such as petunidin and malvidin; Supplementary Table S2). Therefore, it is reasonable to conclude that the ABP in grape hyacinth is unlike the pathways used in many other blue flowers in that it relies mainly on glycosylation and hydroxylation rather than methoxylation to maintain the stability of its blue pigments (Yoshida et al., 2009). Moreover, an average of 71% of the full-length sequences for each of the ABP genes were obtained (Supplementary Table S1). These genes were thus the focus of further study. Table 1. Candidate genes related to flower pigmentation of M. armeniacum Function Gene Enzyme KO id (EC no.) No. All a No. Up b No. Down c Anthocyanin biosynthesis CHS Chalcone synthase K00660 (2.3.1.74) 17 3 4 CHI Chalcone isomerase K01859 (5.5.1.6) 3 0 1 F3H Flavanone 3-hydroxylase K00475 (1.14.11.9) 3 0 0 F3′H Flavonoid 3′-hydroxylase K05280 (1.14.13.21) 7 0 1 F3′5′H Flavonoid 3′,5′-hydroxylase K13083 (1.14.13.88) 4 1 1 DFR Dihydroflavonol 4-reductase K13082 (1.1.1.219) 18 3 1 ANS Anthocyanidin synthase K05277 (1.14.11.19) 4 0 1 UFGT Anthocyanidin 3-O-glucosyltransfersae K12930 (2.4.1.115) 25 0 9 Anthocyanin modification UGT75C1 Anthocyanin 5-O-glucosyltransferase K12338 (2.4.1. 298) 1 0 0 5AT Anthocyanin 5-aromatic acyltransferase K12936 (2.3.1.153) 3 0 1 GT1 Anthocyanidin 5, 3-O-glucosyltransferase K12938 (2.4.1.–) 12 1 0 3′GT UDP-glucose:anthocyanin 3′-O-beta-glucosyltransferase K12939 (2.4.1.238) 4 0 0 5MaT1 Anthocyanin 5-O-glucoside-6′′′-O- malonyltransferase K12934 (2.3.1.172) 2 0 0 Flavone and flavonol biosynthesis FNS Flavone synthase K13077 (1.14.11.22) 5 2 0 FLS Flavonol synthase K05278 (1.14.11.23) 10 0 2 C12RT1 Flavanone 7-O-glucoside 2′′-O-beta-l-rhamnosyltransferase K13080 (2.4.1.236) 5 0 0 FOMT Flavonol 3-O-methyltransferase K05279 (2.1.1.76) 6 0 0 CROMT2 Myricetin O-methyltransferase K13272 (2.1.1.149) 3 0 1 LuOMT Luteolin O-methyltransferase \ d (2.1.1.75) 1 0 0 F4ST Flavonol 4′-sulphotransferase K13271 (2.8.2.27) 2 0 0 GUSB beta-Glucuronidase K01195 (3.2.1.31) 2 0 0 UF3GT Flavonol 3-O-glucosyltransferase K10757 (2.4.1.91) 5 0 0 Flavanone biosynthesis ANR Anthocyanidin reductase K08695 (1.3.1.77) 1 0 0 a No. All, the total number of uni-transcripts analysed. b No. Up, the number of uni-transcripts with expression significantly up-regulated in blue flowers of M. armeniacum compared with in white flowers. c No. Down, the number of uni-transcripts with expression significantly down-regulated in blue flowers of M. armeniacum compared with in white flowers. d ¥, omission of numbers for the KO id. Comparison of transcriptional profiles of genes involved in anthocyanin metabolism between M. armeniacum and M. armeniacum f. album Previous research has demonstrated that the colour difference between white and blue flowers of M. armeniacum is due to the loss of flower anthocyanins (Del and Cy). The shift from blue to white requires a complete blockage of the ABP, which probably occurs in some reaction before Del and Cy are formed. Therefore, the abundance of the ABP candidate genes was compared in M. armeniacum and M. armeniacum f. album transcriptomes to find the key transcripts of blue colour metabolism. Core genes in the pathway were studied in detail, and the results demonstrated that most of the uni-transcripts with significant changes in expression level, regardless of whether they were early [chalcone isomerase (CHI), etc] or late genes (ANS, UFGT, etc.), showed higher transcript abundance in white flowers than in blue flowers (Fig. 2A, B). Interestingly, this result is in sharp contrast to the results of some other studies. In many cases, changes in anthocyanin accumulation have corresponded to changes in expression of genes encoding pathway enzymes (Castellarin and Gaspero, 2007; Wang et al., 2010; Feng et al., 2012; Yuan et al., 2013). To elucidate this matter, the metabolomic profiles of blue petals were compared with those of white petals. A large quantity of flavonoid compounds was detected in white petal extracts, many of them sharing the same intermediates or enzymes with anthocyanin. For example, the contents of myricetin and kaempferol are two and three times greater, respectively, in white petals (Fig. 1G). Common enzymatic steps shared by the biosynthesis of these compounds and anthocyanins are catalysed by chalcone synthase (CHS), flavanone 3′-hydroxylase (F3′H), flavonoid 3′5′-hydroxylase (F3′5′H), etc. (Fig. 2A). This could be the reason why anthocyanin content was not correlated with the expression of anthocyanin biosynthetic genes in grape hyacinth. Fig. 2. Schematic of physiological and metabolic data related to flower colour development of M. armeniacum. (A) A detailed part of the Del and Cy metabolic subnetwork showing the subset of nodes or metabolites that constitute the process. Enzyme names and expression patterns are indicated at the side of each step. The expression pattern of each uni-transcript is shown on two grids, with the left one representing the RPKM value of blue flowers, and the right one representing the relative log2 (expression ratio) of white flowers. The grids with eight different grey scale levels show the absolute expression magnitude of blue flowers, with the RPKM values 0–10, 10–20, 20–40, 40–80, 80–160, 160–320, 320–640, and 640–1280 represented by grey scale levels 1–8, respectively. (B) Transcript accumulation measurements of colour-related genes involved in the anthocyanin metabolic process. (C) Correlation of gene expression results obtained from q-PCR analysis and RNA-Seq for colour-related genes in blue and white flowers. ANR, anthocyanidin reductase; ANS, anthocyanidin synthase; CHI, chalcone isomerase; CHS, chalcone synthase; DFR, dihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; F3′5′H, flavonoid 3′5′-hydroxylase; FLS, flavonol synthase; LAR, leucoanthocyanidin reductase; UFGT, anthocyanidin 3-O-glucosyltransferase. (This figure is available in colour at JXB online.) Candidates which are responsible for the loss of blue colour in grape hyacinth with white flowers Even though most Del- and Cy-related reactions may share the same enzymes, not enough is known about how and when they catalyse the corresponding reactions. Accordingly, each event was treated independently. Of all uni-transcripts involved in the Del biosynthesis process, only three CHS, three DFR, and one F3′5′H homologous sequences showed significantly up-regulated expression in blue flowers; these are thought to be the flux-limiting genes leading to Del elimination in white grape hyacinth. It is generally known that CHS catalyses the first reaction for anthocyanin biosynthesis and helps to form the intermediate chalcone, the primary precursor for all classes of flavonoids (Koes et al., 1989). So if CHS reactions are strongly constrained, not only anthocyanin production but also that of nearly all other flavonoids is effectively eliminated (Clark et al., 2011). On the other hand, F3′5′H plays critical roles in the flavonoid biosynthetic pathway, and catalyses the hydroxylation of the B-ring of flavonoids and is necessary to biosynthesize Del (violet to blue)-based anthocyanins (Tanaka and Brugliera, 2013). It was expected that, in the event that the minimal Del path was cut off from F3′5′H, myricetin-related flavonols would be removed along with Del. In fact, however, a great deal of myricetin was found in white flowers, more than twice as much as in blue flowers (Fig. 1G). Yet this is not a satisfactory explanation for the lack of Del in white grape hyacinth. Hence DFR, a crucial later gene for anthocyanin formation, was considered. As shown in Fig. 1, DFR reduces dihydroflavonols to colouress leucoanthocyanidins, which are catalysed by ANS to coloured anthocyanidins. No products of the Del synthesis route that occur after dihydromyricetin (the substrate for the DFR enzyme) were detected in white flowers (Fig. 3), suggesting that DFR was the most likely target for Del suppression in M. armeniacum f. album. It is noteworthy that the transcripts of three DFR-like sequences showed significantly higher levels of gene transcripts in blue flowers than in white flowers, in some cases >1000 times higher (Fig. 2A). Although this was unexpected, it is a reasonable explanation for the fact that the Del synthesis reactions are constrained to zero. Additionally, the dihydroflavonols represent a branch point in flavonoid biosynthesis, being the intermediates in the production of both the coloured anthocyanins, through DFR, and the colourless flavonols, through flavonol synthase (FLS) (Davies et al., 2003). As a result of the competition for substrate (dihydroflavonols), the up-regulation of FLS and flavonols might be closely accompanied by a decrease in DFR and anthocyanin accumulation. In support of this, inhibition of FLS production through the introduction of an FLS antisense RNA construct led to anthocyanin production and gave the white-flowered petunia a novel pink hue (Davies et al., 2003). In the present study, the abundance of myricetin (a downstream flavonol product of dihydromyricetin) and that of two FLS-like sequences were far greater in white grape hyacinth than in the blue-flowered strain, confirming the hypothesis by another approach. Combining the information with data from HPLC, it could be inferred that DFR might be the target gene for the loss of blue pigmentation (Del) in white grape hyacinth. In addition, strong competition between FLS and DFR for common dihydromyricetin substrates might partially block the synthesis of Del and cause the production of other flavonoid compounds such as myricetin, thereby furthering the process of elimination of blue pigmentation and shifting the flavonol:anthocyanin ratio in M. armeniacum. Fig. 3. A model for the process of Del elimination in the white flowers of M. armeniacum. When DFR is suppressed, the substrates used for Del synthesis are then available for synthesis of myricetin and kaempferol. Moreover, an increase of flavonol production occurs through the up-regulation of FLS, furthering the process of blue pigmentation elimination in the white flowers of M. armeniacum. The global output from the minimal anthocyanin subnetwork in flowers of M. armeniacum was considered to be 100% and was used to define the relative level of each product. The black boxes indicate the genes or the compounds which had a higher relative abundance in white flowers of M. armeniacum than that in blue flowers. The grey boxes indicate the genes or the compounds which had a lower abundance in white flowers than that in blue flowers. CHI, chalcone isomerase; CHS, chalcone synthase; Cy, the global output from the minimal cyanidin subnetwork; DFR, dihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3′5′H, flavonoid 3′5′-hydroxylase; FLS, flavonol synthase. (This figure is available in colour at JXB online.) Reasons for loss of red Cy accumulation in white-flowered grape hyacinth To select the target genes for Cy suppression in grape hyacinth, the expression and metabolomic profiles of blue and white petals were compared in whole Cy metabolic reactions. The presence of catechin and epicatechin in white petals indicated that the red Cy must be present in the white flowers, even if only for a very short time or in a very small quantity, hinting at a complex metabolic mechanism underlying the loss of Cy pigmentation. There may be multiple reasons for this phenomenon. First, DFR and FLS were good candidates for the limitation of Cy accumulation, as discussed earlier. When FLS is up-regulated, the substrates used for Cy synthesis are then available for synthesis of kaempferol in white flowers (Fig. 4). The down-regulation of DFR could decreased Cy production, but obviously it cannot produce a complete blockage of the process on its own. Secondly, the metabolism of Del plays a particularly important role in the flower coloration system of M. armeniacum, whereas the metabolism of Cy is less significant (Fig. 4). In blue flowers, the total content of Del (blue) was three times higher than that of Cy (red), which might also explain why blue is the predominant colour hue in M. armeniacum flowers. Even in white flowers, the 44% yield from the Del metabolic pathway was much higher than the 3% yield from the Cy metabolic pathway (Fig. 4). The low level of productive forces might limit the flux through Cy metabolism in grape hyacinth and explain the small amounts of Cy that accumulate in the white flowers. Thirdly, as is known, the last product before Cy formation is leucyanidin, which can generate two different products, colourless catechin and red Cy, in reactions catalysed by leucoanthocyanidin reductase (LAR) and ANS, respectively. In M. armeniacum, catechin was detected only in white flowers and not in blue flowers (Fig. 4). Therefore, it could be concluded that the alteration in competition from LAR for the substrate might redirect Cy biosynthesis towards catechin and further restrict the flux through its subsequent biosynthesis process. Fourthly, the next step after Cy formation should convert unstable anthocyanin to stable coloured compounds, but the white flowers contain increased concentrations of epicatechin and undetectable levels of Cy (Fig. 4). It is suggested that the low amounts of Cy might be reduced to colourless epicatechin by anthocyanidin reductase (ANR) and thus redirect anthocyanin biosynthesis away from the production of stable Cy-based pigments. Above all, the limitation of flux in upstream reactions and the multishunt process in downstream reactions led to the process of elimination of red pigmentation in the white flowers of M. armeniacum. Fig. 4. A model for Cy elimination in white flowers of M. armeniacum. The fluxes through Cy metabolism were limited. The multishunt process in downstream reactions further promoted Cy turnover and degradation in white flowered grape hyacinth. The global output from the minimal anthocyanin subnetwork in flowers of M. armeniacum was considered as 100% and was used to define the relative level of each product. The black boxes indicate the genes or the compounds which had a higher relative abundance in white flowers of M. armeniacum than that in blue flowers. The grey boxes indicate the genes or the compounds which had a lower abundance in white flowers than that in blue flowers. ANR, anthocyanidin reductase; ANS, anthocyanidin synthase; CHI, chalcone isomerase; CHS, chalcone synthase; Del, the global output from the minimal delphinidin subnetwork; DFR, dihydroflavonol 4-reductase; F3H, flavanone 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; FLS, flavonol synthase; LAR, leucoanthocyanidin reductase; UFGT, anthocyanidin 3-O-glucosyltransferase. (This figure is available in colour at JXB online.) Recently, Clark et al. (2011) considered the advantages of targeting DFR in order to eliminate floral pigmentation: the production of only a few compounds is affected; it does not operate too late in the ABP pathway; it is more essential for anthocyanin production than are other earlier genes, etc. It seems to be a very attractive means, for both plants and breeders, by which to change flower colour from blue to white by the down-regulation of a single DFR. Nevertheless, it seems that such loss-of-colour adaptations are relatively unconstrained in different species because they can be achieved in many ways. For example, the mutation of a single CHS enzyme is often observed. It leads to white flower lines in the petunia (Saito et al., 2006; Spitzer et al., 2007), violet (Hemleben et al., 2004), and arctic mustard flower (Dick et al., 2011). Blocking an early-acting gene such as CHS could be more efficient. Perhaps this is why CHS mutation is the most common means of producing loss of colour in the literature (Clark and Verwoerd, 2011). Another common reason for pigmentation loss is the absence of more than one enzyme in the ABP, such as ANS and DFR (Ma et al., 2004; Bogs et al., 2007; Clark and Verwoerd, 2011). Recent research has described many new ways to determine the lack of colour phenotype by regulating the branching point of anthocyanin biosynthesis. For instance, inhibition of ANR and consequent LAR production by the transient suppression of the FcMYB1 gene in white strawberry fruit leads to increased concentrations of anthocyanins and undetectable levels of flavan-3-ols (Salvatierra et al., 2013). Similarly, introduction of apple ANR genes into tobacco inhibits expression of both CHI and DFR genes in flowers, finally leading to loss of anthocyanin (Han et al., 2012). Here, a new hypothesis is proposed explaining a lack of colour phenotype of grape hyacinth flowers. The truth of the matter is probably more complex than what has been described here, the elucidation of which could be an interesting and challenging subject. Supplementary data Supplementary data are available at JXB online. Figure S1. KEGG reference mappings for flavonoid synthesis, anthocyanin biosynthesis, and flavone and flavonol biosynthesis pathways. Table S1. List of relative uni-transcripts in the three secondary metabolic pathways in the M. armeniacum transcriptome. Table S2. The contents of flavonoids in flower petals of M. armeniacum. Table S3. Length and gap distribution of contigs, scaffolds, and uni-transcripts from each library of M. armeniacum. Supplementary Data
- Record: found
- Abstract: found
- Article: not found
Integration of light- and brassinosteroid-signaling pathways by a GATA transcription factor in Arabidopsis.
Hui Lin, Min Luo, Shengwei Zhu … (2010)
Light and brassinosteroid (BR) antagonistically regulate the developmental switch from etiolation in the dark to photomorphogenesis in the light in plants. Here, we identify GATA2 as a key transcriptional regulator that mediates the crosstalk between BR- and light-signaling pathways. Overexpression of GATA2 causes constitutive photomorphogenesis in the dark, whereas suppression of GATA2 reduces photomorphogenesis caused by light, BR deficiency, or the constitutive photomorphogenesis mutant cop1. Genome profiling and chromatin immunoprecipitation experiments show that GATA2 directly regulates genes that respond to both light and BR. BR represses GATA2 transcription through the BR-activated transcription factor BZR1, whereas light causes accumulation of GATA2 protein and feedback inhibition of GATA2 transcription. Dark-induced proteasomal degradation of GATA2 is dependent on the COP1 E3 ubiquitin ligase, and COP1 can ubiquitinate GATA2 in vitro. This study illustrates a molecular framework for antagonistic regulation of gene expression and seedling photomorphogenesis by BR and light. Copyright © 2010 Elsevier Inc. All rights reserved.
Author and article information
Journal
Journal ID (nlm-ta): Molecules
Journal ID (iso-abbrev): Molecules
Journal ID (publisher-id): molecules
Title:
Molecules
Publisher:
MDPI
ISSN
(Electronic):
1420-3049
Publication date
(Electronic):
02
January
2020
Publication date Collection:
January
2020
Volume: 25
Issue: 1
Electronic Location Identifier: 190
Affiliations
[1
]Department of Tea Science, Qiannan Normal University for Nationalities, Duyun 558000,
China;
teasky@
123456foxmail.com
(C.Z.);
yzb1976110@
123456sohu.com
(Z.Y.)
[2
]College of Horticulture, South China Agricultural University, Guangzhou 510640, China;
Dylan.Rothenberg@
123456colorado.edu
(D.O.R.);
wendyzhang998@
123456163.com
(W.Z.);
15797708411@
123456163.com
(S.W.);
hjyang@
123456scau.edu.cn
(H.Y.)
[3
]South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China;
xmei@
123456scbg.ac.cn
Author notes
Author information
Caibi Zhou
https://orcid.org/0000-0003-4218-6408
Dylan O’Neill Rothenberg
https://orcid.org/0000-0003-2463-0317
Lingyun Zhang
https://orcid.org/0000-0001-5733-1810
Article
Publisher ID: molecules-25-00190DOI: 10.3390/molecules25010190
PMC ID: 6983220
PubMed ID: 31906542
SO-VID: 06ac44fa-6f27-4531-9ee1-87a7c232333c
Copyright © © 2020 by the authors.
License:
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).
History
Date
received
: 27
November
2019
Date
accepted
: 31
December
2019
Categories
Subject:
Article
Data availability: