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Combined omics unravels the molecular mechanism of golden-leaf coloration in
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Koelreuteria paniculata ‘jinye’
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Ting Guo1,†, Ruqian Wu1,†,Xiong Yang1, Sai Huang1,Deyu Miao1,Tingting Chen1,Yinxuan Xue1,4
Juan Li1, Kai Gao1, Bin Guo2, Xinmin An1,*
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1. Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering7
Research Center of Tree Breeding and Ecological Restoration, College of Biological Sciences and8
Technology, Beijing Forestry University, Beijing 100083, China.9
2. Shanxi Academy of Forestry and Grassland Sciences, Taiyuan 030012, China10
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†: Contributed equally to this work.12
*: To whom correspondence may be addressed: X.A. (email: anxinmin@bjfu.edu.cn)13
Xinmin An and Ting Guo designed the experiments. Ting Guo, Ruqian Wu drafted the manuscript.
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Ting Guo, Ruqian Wu, Xiong Yang, Sai Huang and Deyu Miao performed experiments. Bin Guo
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and Yinxuan Xue collected experimental materials. Juan Li, Kai Gao and Tingting Chen analyzed
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the transcriptomic data. All authors read and approved the final manuscript.
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Abstract27
Koelreuteria paniculata is widely distributed in Asia and introduced to Europe and North America. K. paniculata
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‘jinye’ is a mutant variety used in landscaping that has a golden leaf color phenotype. Although similar leaf color
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variants occur in plants, little is known of the underlying mechanism. We performed physiological, anatomical,
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microRNA sequencing, transcriptomic, and metabolomic analyses of the golden leaf variation in the mutant.
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Compared with the original green cultivar, the golden leaf mutant exhibited 76.05% and 44.32% decreased
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chlorophyll a(Chl a) and chlorophyll b(Chl b) contents, respectively, and significantly increased carotenoid
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content. Analysis of leaf ultrastructure revealed an abnormal chloroplast morphology and fewer lamellae in the
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mutant. Fifty-nine differentially expressed genes (DEGs), forty transcription factors (TFs) and forty-nine
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differentially expressed miRNAs (DEmiRs) involved in pigment metabolism, chloroplast development, and
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photosynthesis were identified. The GLK and petC genes were downregulated and are involved in chloroplast
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development and chlorophyll synthesis, respectively. The upregulated PSY and PDS genes, and the downregulated
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NCED gene promote carotenoid accumulation. A variety of chalcones and flavonols were upregulated in the
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mutant. Consequently, the carotenoid to chlorophyll ratio increased by more than 75%, and the accumulation of
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chalcones and flavonols was responsible for the golden leaf phenotype of the mutant K. paniculata.
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Introduction
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Leaf color mutants are widely used for landscaping. They have bright colors, long viewing cycle, can be used to
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form large color blocks, and replace flowers with leaves in the light flower season. Therefore, leaf color mutant
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plants—such as Ulmus pumila ‘Jinye’, Acer rubum,Cotinus coggygria,Populus deltoids ‘Quanhong’, and
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Photinia fraseri—are preferred for landscaping and road greening (Zhang et al., 2017). Although the genetic
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patterns of colored-leaf plants are varied, at the metabolic level, the leaf color of higher plants depends on the
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content and ratio of pigments (mainly including chlorophyll, carotene, and flavonoids) in leaves. Chlorophyll is an
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important photosynthetic pigment, and mutations in genes linked to its biosynthesis, chloroplast protein transport,
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or the plant pigment regulation pathway (Wen et al., 2016; Zhang et al., 2017) can result in leaf coloration mutants.
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Plant carotenoids range in color from yellow to red, are involved in light harvesting, and are indispensable for
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photoprotection against excess light. An increase in the carotenoid-to-chlorophyll ratio may result in yellow leaf
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traits, such as the yellow-striped leaves of a Ginkgo biloba L mutant (Li et al., 2018). Anthocyanins are naturally
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occurring pigments responsible for the red, purple, and blue color of plant organs, and are essential in multiple
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biological processes. The formation of red leaves is the result of anthocyanin accumulation (Li et al., 2015).
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Therefore, studies of the molecular mechanism of leaf color mutants have focused on pigment metabolism.
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Koelreuteria paniculata is an arbor species widely distributed in Asia and introduced to Europe and North
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America. This species is strongly adaptable to the environment and suitable for phytoremediation in heavy
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metal-contaminated areas (Tian et al., 2009). Its crude extract has medicinal and antimicrobial properties, and its
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leaves are used as antifungal and antibacterial agents by local people (Yang et al., 2018). Because of its rich
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flavonoids, K. paniculata is used in both medicine and landscaping (Kim et al., 2017; Lyu et al., 2017). K.
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paniculata ‘jinye’ is a new variety of K. paniculata bred by seedling mutation. Its physiological characteristics are
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similar to those of K. paniculata except that the golden leaves remain yellow during the growing period,
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enhancing its ornamental value. It also serves as a source of materials for study of the molecular mechanism and
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secondary metabolites of the K. paniculata leaf-color mutation.
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The integration of metabolomics and transcriptomics can reveal the biosynthetic mechanisms of key
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differential functional pathways in plants (Li et al., 2020). For example, metabolomics and transcriptomics
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revealed the metabolic and transcriptional differences between the ‘Rougui’ protogreen leaf variety and its yellow
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leaf mutant, deepening our understanding of the mechanism of tea leaf coloration (Wang et al., 2020).
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Investigation of changes in the transcriptome and metabolome of jujube peel at various maturity stages and the
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mechanism of jujube peel coloring revealed the metabolic pathways and genes linked to jujube peel color change
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(Zhang et al., 2020). Indeed, combined -omics methods are now widely used in plant research.
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To gain insight into the biological basis of leaf color and metabolite changes, we performed metabolomic,
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RNA-seq, microRNA sequencing and iso-seq analyses of the physiological and transcriptomic characteristics of
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golden leaf coloration in K. paniculata. Our findings provide reference information for studies of leaf coloration
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in other plant species.
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Results
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Cytological changes in mutant leaves89
We compared chloroplast ultrastructure in normal and mutant leaves. In healthy plants, the chloroplast was oblong90
or fusiform, close to the cell membrane, the stromal lamella and the basal lamella were closely arranged, and the91
lamellae were stacked neatly. There were a few osmophilic granules and starch granules on the surface, and the92
chloroplast membrane was intact (Fig. 1a, b). In mutant leaves, chloroplast morphology was abnormal, the93
thylakoid structure was disrupted, and the grana lamella was loose and broken or missing (Fig. 1d).94
Correspondingly, some chloroplasts contained irregularly arranged vesicles. Furthermore, there were fewer starch95
granules in chloroplasts of GL, but these were filled with numerous plastoglobuli (Fig. 1c), indicating a low96
photosynthetic capacity and a disrupted chloroplast membrane.97
Physiological changes in GL leaves
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We analyzed changes in the pigment contents of WT and GL (Fig. 2). Compared with the WT, the contents of Chl
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aand Chl bin leaves of GL in May decreased by 87.38% and 44.19%, respectively; the total chlorophyll content
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decreased by 72.81%, and the anthocyanin content decreased by 10%. The content of carotenoids increased by
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26.43%; the carotenoid-chlorophyll ratio in the WT was 0.35, which was significantly lower than in the mutant
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leaves (1.63). In July, the Chl aand Chl bcontents in K. paniculata ‘jinye’ leaves decreased by 76.87% and
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44.32%, respectively, compared with the WT whereas the contents of carotenoids increased by 30.22%, and the
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contents of anthocyanins remained unchanged. The carotenoid-chlorophyll ratio in GL increased by 78.49%.
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Compared with the WT, the Chl a, Chl b, and anthocyanin contents in GL decreased significantly in September,
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whereas the carotenoid content and carotenoid-chlorophyll ratio increased significantly. In May and September,
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there was no significant difference in the contents of chlorophyll intermediates between WT and mutant leaves
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(Fig. S1), but in July, the Urogen Ⅲ and Pchlide contents increased significantly, whereas that of coprogen Ⅲ
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decreased significantly.
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Using LC-MS with PCA and PLS-DA analyses (Fig. S2), we detected a total of 1471 metabolites. Using a
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VIP ≥ 1 and fold change ≥ 2 or fold change ≤ 0.5 as criteria, 128 significantly changed metabolites (SCMs) were
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detected and grouped into 11 classes (Table S2). Among them, the abundances of 95 and 33 SCMs were increased
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and decreased, respectively. Organic acids, lipids, and oxygenous organic compounds were major contributors to
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the separation between the two samples. These are likely related to the basic growth metabolism of plants caused
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by leaf color changes. However, phenylpropane and polyketides were upregulated in GL (Fig. S3). To assess their
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contribution to leaf color variation, we generated a clustering heat map of 128 SCMs using the TBtools method
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(Fig. 3). The GL contained higher levels of flavonoids. Notably, the top enriched KEGG terms among the SCMs
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detected were phenylpropanoid biosynthesis, glutathione metabolism, and flavone and flavonol biosynthesis (Fig.
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S4). Also, chalcone, an important intermediate of flavonoid metabolites, and several flavonols, but not
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anthocyanins, accumulated significantly.
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DEmiRs related to pigment metabolism and predicted target genes in WT and GL
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Most DEmiRs and their predicted targets were primarily identified between WT and GL in pigment metabolism
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pathway. Further, the correlation between DEmiRs and DEGs was analyzed. A total of 77 relevant miRNA-mRNA
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interaction pairs, including 45 in the chlorophyll metabolic pathway (Fig. 4a), 14 in the carotenoid metabolic
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pathway (Fig. 4b), and 18 in the flavonoid synthesis pathway (Fig. 4c), were predicted. Further, analyses were
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carried out to identify whether these interactions were either coherent (the expression level of target mRNA is
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more when that of miRNA is less; the “DU” and “UD” patterns) or non-coherent (miRNA and its target mRNA
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have similar expression profiles) (Li et al., 2022).
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Analyses revealed that 77 miRNA-mRNA pairs, involving 49 miRNAs and 27 targets, were associated
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expressed with transcriptome data between WT and GL (Table S5, S6, S7). In most combinations, the higher the
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target gene expression, the lower the microRNA expression, for example miR166b-GLK, miR3711a-petC pairs
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etc.; a few combinations indicated similar microRNAs and target genes expression, for example miR157d-psaO
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etc..
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Overview of SMRT and Illumina sequencing135
Using SMRT sequencing technology, 20 Gb of subreads were detected in the roots, stems, leaves, flowers, and
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seeds of K. paniculata. By filtering based on a length > 50 bp, full passes ≥ 0, and quality > 0.80, we screened
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355,606 RoIs (Fig. S5), including 93.81% (333,598) FLNC reads and 3.80% (13,512) NFL reads (Table 1). The
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FLNC sequences were clustered to obtain nonredundant isoforms for use as a reference for sequence alignment.
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The clustering sequence statistics are shown in Table S3.
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From WT and GL leaves, we obtained 38.34–41.38 million raw reads, which yielded 35.57–38.32 million
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clean reads after quality control. The Q30 of the raw reads ranged from 89.1% to 91.69%, indicating the high
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quality of the transcriptome data (Table 2). Furthermore, 79,393 unigenes were annotated in six databases (Fig. 5b,
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c, d; Fig. S6). In the NR database, 75,344 annotations were obtained, accounting for 94.9%; the minimum number
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of annotations in the GO database was 26,954, accounting for 33.95%; 13.73% of the unigenes were annotated in
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all databases (Table S4).
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Identification and verification of DEGs147
A differential expression analysis yielded 3793 DEGs in the leaves of WT compared to GL, including 2190
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upregulated and 1630 downregulated genes (Fig. 5a).
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GO analysis indicated that among biological processes, “carbohydrate metabolic process” and “inositol
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biosynthetic process” were the top enriched terms, whereas among cellular components, most DEGs were
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enriched in “cell wall” and “external encapsulating structure.” Among molecular functions, most DEGs were
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enriched in “catalytic activity” and “inositol-3-phosphate synthase activity” (Fig. S7). We speculate that a change
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in leaf color affects photosynthetic efficiency and alters physiological metabolism. “Organic acid metabolic
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process”, “L-phenylalanine metabolic process”, and “tetrapyrrole binding” were the major leaf color-related GO
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terms across all GO categories. Among these, tetrapyrrole binding is related to chlorophyll synthesis. “Organic
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acid metabolic process” and “L-phenylalanine metabolic process” are upstream pathways of flavonoid synthesis.
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In short, in response to leaf color mutation, there may be a variety of changes in biological response in K.
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paniculata.
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The KEGG pathway analysis showed a significant separation between the WT and GL, indicating that
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changes in metabolite accumulation during development are tightly governed by differential gene expression. The
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WT vs. GL DEGs mapped to 121 KEGG pathways. Among them, primary metabolic processes were significantly
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enriched, demonstrating that leaf-color changes affect plant growth and development. Six pathways related to leaf
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color were identified: “phenylpropane biosynthesis”, “flavonoid biosynthesis”, “porphyrin and chlorophyll
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metabolism”, “carotenoid biosynthesis”, “photosynthesis”, and “photosynthetic antenna protein”. Of the top
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KEGG enrichment pathways, two were related to flavonoid metabolism (“phenylpropanoid biosynthesis” and
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“flavonoid biosynthesis”) (Fig. 5e). In mutant leaves, many downregulated DEGs were enriched in “energy
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metabolism” and “carbohydrate metabolism” whereas several upregulated DEGs were enriched in “porphyrin and
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chlorophyll metabolism” and “carotenoid biosynthesis.” A few DEGs were enriched in the photosynthesis
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pathway.
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DEGs and SCMs related to pigment metabolism in WT and GL171
Normal green leaves depend on the balance of chlorophyll, carotenoids, and flavonoids. Although the content of
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Coprogen III in yellow leaves was about 0.77-fold that of green leaves, the contents of other intermediates such as
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UrogenⅢ, Proto Ⅸ, Mg-ProtoIX, and Pchlide were not decreased in yellow leaves. This is consistent with the
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finding that 17 DEGs annotated in the porphyrin and chlorophyll metabolism pathway were upregulated in mutant
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leaves (Fig. 6a). However, the Chl aand Chl bcontents were lower in yellow leaves compared to green leaves.
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RNA-seq showed that the expression of CHLG, which catalyzes chlorophyllide production, was not significantly
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affected in yellow leaves. GLK (golden2-like) genes are key regulators of chloroplast development. In this study,
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the expression of two DEGs annotated as GLKs was lower in yellow leaves. According to the KEGG pathway
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results, 15 unigenes in the photosynthesis pathway were annotated as 10 genes. Compared with normal green
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leaves, the expression levels of psaA,psaF, and psaO (related to the reaction center subunit of photosystem I
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[PSI]), as well as ATPB (related to F-ATPase) were upregulated. However, the mRNA levels of ATPD,ATPG, and
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petC (associated with the cytochrome b6-f complex) were significantly downregulated, and petC was a highly
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significantly downregulated.
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The total carotenoid content differed significantly between WT and GL, and carotenoid composition may
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influence leaf coloration. We identified 15 DEGs regulating carotenoid biosynthesis and degradation based on
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KEGG pathway annotations (Fig. 6b). Two genes involved in carotenoid metabolism were upregulated in mutant
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leaves; one was annotated as PHYTOENE SYNTHASE (PSY) and the other as 15-CIS-PHYTOENE
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DESATURASE (PDS). Nine carotenoid cleavage dioxygenase (CCD) genes encode synthetic proteases, which
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mediate oxidative degradation of carotenoids. Three CCD1/CCD4/NCED genes, which are closely related to
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carotenoid degradation, were significantly downregulated in the mutant. ABA2,CYP707A1, and CYP707A4 were
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related to the biosynthesis of abscisic acid (ABA) downstream of the carotenoid synthesis pathway, and all were
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downregulated. The upregulation of carotenoid biosynthesis genes and downregulation of carotenoid degradation
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genes likely lead to carotenoid accumulation in mutant leaves.
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Based on the KEGG enrichment results, 2 of the top 10 metabolic pathways were related to flavonoid
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synthesis. We constructed a network to assess the relationships between gene expression and metabolite
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accumulation (Fig. 6c). Compared with the WT, the abundance of cinnamic acid and p-coumaric acid in GL was
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increased. The intermediates naringenin and kaempferol, as well as other flavonoids and flavonols, accumulated
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significantly in mutant leaves. We identified 17 DEGs associated with flavonoid biosynthesis. The expression of
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nine DEGs was upregulated in GL leaves, including flavonoid synthesis precursor synthase genes (e.g.,PAL,4CL,
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and CYP73A), early biosynthesis genes (e.g., CHS, CHI), and a late biosynthesis gene anthocyanin biosynthesis
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gene (DFR). This is consistent with the higher flavonoid and flavonol contents in the leaves of GL.
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Transcription factors involved in leaf coloration203
In this study, 381 DEGs were identified as transcription factors (TFs) belonging to 39 TF families, some of which
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were significantly upregulated and downregulated. Therefore, leaf-color variation affects the regulation of gene
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expression via TFs. The most abundant TF family was the MYB ultrasound family (53, 13.91%), followed by the
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C3H (36, 9.45%), NAC (30, 7.87%), ERF (27, 7.09%), bHLH (19, 4.99%), and WRKY (17, 4.46%) families (Fig.
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S9). MYB and bHLH transcription factors regulate flavonoid biosynthesis. In this study, the MYB family was
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represented by 43 DEGs upregulated in GL. Also 19 DEGs related to bHLH transcription factors were identified,
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most of which were upregulated in GL. In addition, the expression of DEGs encoding WRKY, TCP, and C2H2
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family TFs was significantly higher in GL leaves than in WT leaves.
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Verification of DEGs by RT-qPCR212
To verify the transcriptome data, we performed RT-qPCR analysis of selected DEGs at three developmental stages213
(May, July, and September). In total, 15 DEGs were detected, comprising 5 genes involved in chlorophyll214
biosynthesis, 2 in chlorophyll development, 2 in photosynthesis, 3 in carotenoid metabolism, and 3 genes involved215
in flavonoid biosynthesis. The expression levels of 12 genes detected by RT-qPCR showed patterns similar to the216
transcriptome data (Fig. S10). In May, July, and September, the expression levels of several genes in the mutant217
were significantly decreased, such as GLK and FtsH (related to chloroplast development) and NCED (related to218
carotenoid degradation) (Fig. S10h, i, l). PDS and CYP73A were upregulated in the mutant (Fig. S10k, n).219
Compared with the WT, HEMA (involved in chlorophyll biosynthesis) was significantly upregulated in GL in May220
and September but downregulated in July (Fig. S10a). HEMC was significantly upregulated in May and221
downregulated in July and September (Fig. S10b). In May and July, photosynthesis-related genes were222
significantly downregulated, but significantly upregulated in September (Fig. S10f). By contrast, psaL was223
significantly upregulated in May and July and downregulated in September (Fig. S10g). PSY and DFR were224
consistent with the trend of psaL (Fig. S10j, o). In general, the RT-qPCR results were consistent with RNA-seq225
data, indicating the transcriptome data to be valid and reliable.226
Discussion
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We investigated a yellow leaf mutant of K. paniculata. To explore mechanisms of the leaf color mutation in K.
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paniculata ‘jinye’, numerous color-related genes were identified and their expression patterns were investigated.
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We focused on the genes related to pigment synthesis, chloroplast development, TFs, and photosynthesis based on
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enrichment results of transcriptome and metabolome analyses.
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Chlorophyll metabolism and chloroplast development are responsible for leaf color change
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Chlorophyll is an important pigment in the thylakoid that captures and transfers light energy in photosynthesis
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(Nakajima et al., 2012). Leaf color formation is closely related to chlorophyll metabolism and chloroplast
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development. Similar to other yellow-leaf mutants, K. paniculata ‘jinye’ is a chlorophyll-deficient chlorina mutant.
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Compared with the WT, the Chl aand Chl bcontents of GL were significantly lower by 76.05% and 44.32%,
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respectively. Therefore, the decrease in total chlorophyll content was responsible for the yellow leaf phenotype.
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In higher plants, the synthesis of chlorophyll starts from glutamyl tRNA, and can be divided into three parts:
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the synthesis of 5-aminolevulinic acid (ALA) to protoporphyrin IX is completed in the chloroplast matrix, and the
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synthesis of magnesium protoporphyrin to chlorophyllide is completed in the chloroplast membrane. Finally, Chl
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aand Chl bare synthesized on the thylakoid membrane (Matringe et al., 1992). Most leaf color mutants have an
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altered thylakoid membrane structure. For example, in yellow leaf mutant G. biloba, chloroplast ultrastructure was
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markedly altered, chloroplast-like membranes were broken, vesicles were dense, and there were no inner
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membrane structures (Li et al., 2018). Similarly, an abnormal internal capsule membrane was found in P. deltoides,
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bamboo, and Anthurium andraeanum leaf mutants (Yang et al., 2015). In this study, the chloroplast structure of
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GL differed significantly from that of the WT. The chloroplasts of mutant leaves were irregularly shapes, in the
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basic structure, the thylakoid membranes were ruptured and chloroplast grana were loosely arranged and showed
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fractures and deletions. Also, there were more plastoglobuli and fewer starch particles; the latter indicates
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decreased photosynthetic capacity, and osmium-containing granules indicate disruption of the chloroplast
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membrane. These structural abnormalities in chloroplasts affect photosynthesis and chlorophyll accumulation.
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In this study, precursors of chlorophyll synthesis did not decrease in the mutant, and no gene in the
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chlorophyll biosynthesis pathway showed decreased expression. As an important TF for chlorophyll accumulation
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and chloroplast formation, GLK was significantly decreased in GL, similar to reports of other species (Gang et al.,
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2019). For example, low expression of dmGLK2 and no expression of dmGLK1 inhibited chloroplast development
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and decreased the chlorophyll content, resulting in the yellow-green leaf phenotype of chrysanthemum (Chang,
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2013). A 40-bp deletion mutation in BpGLK1 of birch decreased the chlorophyll content, hampered chloroplast
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development, and induced a yellow-green leaf mutant (Gang et al., 2019). Therefore, a change in chlorophyll
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content may be reflected by abnormalities of plastid development and function.
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Photosynthesis is responsible for the leaf-color change259
Light is the most important environmental factor affecting plant leaf color (Zhang et al., 2019). The leaf color of K.
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paniculata ‘jinye’ was yellow-green in the shade and after shading treatment, the leaf color changed from golden
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yellow to yellow-green. This suggests K. paniculata ‘jinye’ to be a photosensitive mutant. The leaf color of the GL
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mutant is affected by light intensity (Zhang et al., 2019). However, the mechanism of the light response of the GL
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mutant was unclear. Light is necessary for chloroplast morphogenesis. Light controls gene transcription,
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chlorophyll synthesis, and protein degradation, thus regulating chloroplast development. The phenotype of
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chlorophyll-deficient mutants was affected significantly by environmental factors such as light (Song et al., 2017).
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During photosynthesis, light energy is captured by the pigment in the LHC protein and transferred to the reaction
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center complex of photosystem I (PSI) and photosystem II (PSII). The cytochrome b6-f (Cyt b6-f) complex
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responds to the electron flow balance from PSII to PSI via the plastid quinone pool and regulates the activity of
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PSII kinase light-harvesting complex. In a chlorophyll-deficient mutant, LHC protein was significantly reduced or
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lost, hampering granule accumulation in chloroplasts (Kim et al., 2009). In this study, the expression of three
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DEGs in the LHC gene family was decreased in GL, indicating that the light-harvesting chlorophyll protein was
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reduced compared with the reaction center complex. PSI catalyzes light-driven electron transfer from luminosome
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cyanin to matrix ferredoxin, which consists of more than 10 subunits including PsaC and PsaL. The PsaL subunit
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is responsible for most of the interactions, and its mutants show a slightly smaller functional size of the
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photosynthetic antenna and a lower excitation level (Klodawska et al., 2013). Activity of the Cyt b6-f complex,
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which is encoded by chloroplast and nuclear genes, affects the electron transfer rate. The Cyt b6-f complex Rieske
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Fe/S precursor protein of all photosynthetic eukaryotes is encoded by the nuclear petC gene (Veit et al., 2012).
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Site-directed knockout of the petC gene in Arabidopsis thaliana blocks the electron transport chain and produces a
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yellow leaf trait (Maiwald et al., 2003). In a Forsythia GL mutant, petC expression was downregulated and not
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affected by light intensity (Shen, 2019). In this study, psaL and petC expression in the Cyt b6-f complex decreased
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highly significantly (log2foldchange –9.1). Therefore, downregulation of gene expression in photosynthesis may
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explain the decreased chlorophyll content. petC (c69066_g1) is key for the leaf color change in K. paniculata
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‘jinye’.
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Carotenoid biosynthesis is responsible for the leaf color change285
Carotenoids have important effects on photomorphogenesis and photosynthesis (Cazzonelli and Pogson, 2010; Li286
et al., 2009). Carotenoids have multiple conjugated double bonds and can absorb light in the range of 400–500 nm.287
Therefore, the accumulation of carotenoids turns plants yellow, orange, and red (Zhou et al., 2020). Compared288
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with the WT, the carotenoid content in GL leaves increased significantly. Yellow leaf mutants typically have a
289
significantly increased carotenoid content. Carotenoid accumulation and pigmentation are dependent on the
290
expression of carotenoid biosynthesis genes (Hao et al., 2020). As the first committed enzyme in carotenoid
291
synthesis, phytoene synthase (PSY) converts two molecules of geranylgeranyl pyrophosphate (GGPP) to phytoene
292
(Kang et al., 2014). Overexpression of PSY1 increased the carotenoid content of tomato fruit (Fraser et al., 2007).
293
PDS catalyzes the biosynthesis of lycopene from phytoene (Fu et al., 2016). In this study, five DEGs (PSY,PSY1,
294
and PDS) related to carotenoid biosynthesis were upregulated in GL and likely contribute to carotenoid
295
accumulation.
296
Carotenoid accumulation is also related to carotenoid degradation. Plants have two major carotenoid
297
degradation pathways mediated by carotenoid dioxygenase (CCD) and 9-cis-epoxidized carotenoid dioxygenase
298
(NCED). The degradation of carotenoid is suppressed in CCD1 mutant Arabidopsis, the seed of which showed an
299
increased carotenoid content (Schwartz, 2001). Moreover, the β-carotene content of the flesh of a CCD4-deficient
300
mutant changed at the late-ripening stage, accompanied by yellowing (Ma et al., 2014). NCED is the rate limiting
301
step from 9'-cis neoxanthin to ABA. The transcriptional regulation of NCED is a focus of research on ABA
302
metabolism. RNAi-mediated inhibition of NCED expression in tomato increased the lycopene and β-carotene
303
contents of mature fruit and reduced the ABA content (Ji et al., 2014). The mRNA levels of other ABA
304
synthesis-related genes, such as ABA2 and CYP707A, were significantly decreased in GL. As a downstream
305
product of the carotenoid pathway, ABA may be a feedback regulator of carotenoid metabolism (Mohd et al.,
306
2013). Exogenous ABA induces important genes involved in carotenoid metabolism, such as PSY3 in maize and
307
DSM2, a β-Carotene Hydroxylase gene in rice (Du et al., 2010; Li et al., 2008). Moreover, some TFs in citrus,
308
such as CrMYB68, indirectly affect carotenoid metabolism by directly inhibiting ABA biosynthesis. The effect of
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ABA on carotenoid metabolism needs further study.
310
Flavonoid biosynthesis is responsible for leaf color changes311
Flavonoids are an important part of leaf color formation and protect leaves from damage caused by
312
sunlight-derived UV irradiation (Agati et al., 2012). In this study, GO and KEGG analyses identified 17 DEGs in
313
flavonoid metabolic pathways and 10 flavonoid-related DEGs in phenylpropanoid biosynthesis. Most showed
314
significant changes in expression level. The transcript abundances of flavonoid synthesis precursor synthases (e.g.,
315
PAL, 4CL, and CYP73A), EBGs (e.g., CHS and FLS), and LBGs (e.g., DFR) were higher in GL compared to WT.
316
Phenylalanine ammonia lyase (PAL) catalyzes the deamination of L-phenylalanine (L-Phe) to yield cinnamic acid
317
as the first committed step in phenylpropanoid synthesis. 4CL encodes 4-coumaric acid coenzyme A ligase, which
318
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catalyzes the conversion of phenylalanine to 4-coumaric acid coenzyme A as a precursor for flavonoid synthesis.
319
As key enzymes in flavonoid biosynthesis, PAL and 4CL affect the total flavonoid pathway flux through a variety
320
of phenylacetone pathways (Winkel-Shirley, 2001). The upregulation of upstream gene expression may lead to
321
accumulation of reaction substrates (Liu et al., 2019). High expression of EBGs leads to the accumulation of
322
chalcones and flavonols. Although DFR was upregulated, no anthocyanins were found among SCMs. This may be
323
a result of redirection of carbon flux to flavonoid branches, which is consistent with the significant increase in
324
kaempferol content in the GL.
325
As secondary metabolites of phenylpropanes, flavonoids have a basic structure of C6-C3-C6 and are
326
classified into several groups (e.g., chalcones, flavonols, flavones, and anthocyanins). Flavonoids have the widest
327
color range, from light yellow to blue. Anthocyanins are responsible for the plant coloration range from orange to
328
blue and are used as natural food pigments. Chalcones and aurones are the main sources of yellow color in plants.
329
For example, naringenin is widely found in yellow plants, whereas aurones are endemic to Caryophylla (Tanaka et
330
al., 2008). Some flavonols and flavones are light yellow, affecting anthocyanin color development as co-pigments.
331
In this study, most differential metabolites were chalcones, flavonols, and flavones, but not anthocyanins, which
332
explains the yellow color of the leaves. Our results shed light on the regulatory network of flavonoid biosynthesis.
333
The combination of metabolomics and transcriptomics enables investigation of the relationships between key
334
genes and metabolites in biosynthetic pathways. We identified candidate genes and metabolites involved in the
335
flavonoid biosynthesis pathway, providing insight into the leaf color variation of K. paniculata.
336
Conclusions
337
We investigated differences in coloration between normal green leaves of K. paniculata and gold-color leaves of
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K. paniculata ‘jinye’ at the physiological and molecular levels. The mutant leaves showed a decreased
339
chlorophyll-carotenoid ratio and abnormal chloroplast ultrastructure. We identified 3793 DEGs and 128 SCMs in
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GL compared to WT. And 49 DEmiRs were identified in chloroplast development, photosynthesis, and pigment
341
metabolism pathways. Downregulation of genes related to chloroplast development and photosynthesis, such as
342
GLK and petC, resulted in decreased chlorophyll accumulation and enhanced carotenoid biosynthesis, increasing
343
the carotenoid content in mutant leaves. In addition, the high expression of phenylpropanoid and flavonoid
344
pathway genes and the accumulation of flavonoids and flavonols in the mutant may also be related to leaf color
345
formation. We conclude that the formation of leaf color of K. paniculata ‘jinye’ is related to chloroplast
346
development, photosynthesis, and pigment metabolism pathway genes. This leads to accumulation of carotenoids,
347
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flavonoids, and flavonols, and a decrease in chlorophyll content, resulting in a change in leaf color from green to
348
yellow (Fig. 7). Our findings provide a reference for studying the mechanism of plant leaf color variation.
349
Materials and Methods350
Plant materials
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The green-leaf K. paniculata cultivar (wild type, WT) and the yellow-leaf K. paniculata ‘jinye’ cultivar (golden
352
leaf mutant, GL) were used with three independent biological replicates. The plants were grown under natural
353
conditions in the Koelreuteria nursery in Xiangyuan County, Shanxi Province, China.Leaf tissues were collected
354
from May to September in 2018. For cytological analysis, RNA-seq, and metabolomics experiments, green leaves
355
and mutant leaves were sampled separately on July (Fig. 8a-f). For physiological and RT-qPCR experiments,
356
mutant leaves and green leaves collected at three stages (May to September) were used. At least 10 tender leaves
357
at the third position from the top of a branch were sampled from three plants of each type, immediately frozen in
358
liquid nitrogen, and stored at −80C for further analysis.
359
Measurement of chlorophyll and carotenoid contents360
Approximately 0.1 g of wild-type and mutant leaves were cut into pieces and extracted with 15 mL of 80%
361
acetone at 4 C for 24 h in the dark. The extract was measured spectrophotometrically at 470, 646, and 663 nm.362
The chlorophyll a(Chl a), chlorophyll b(Chl b), carotenoid, and cyanidin contents were determined as described
363
elsewhere (Gang et al., 2019). To measure the contents of chlorophyll intermediates, leaves were dissolved in nine
364
volumes of 0.01 M phosphate-buffered saline in an ice bath and centrifuged (30 min at 2500 × g). The supernatant
365
was assayed separately using an ELISA Kit (HengYuan Biological Technology Co., Ltd, Shanghai, China). Three
366
biological replicates were analyzed per sample. Data were analyzed by t-test using SPSS software (ver. 17.0;
367
SPSS Inc., Chicago, IL), and means were compared at significance levels of 0.01 and 0.05.
368
Transmission electron microscopy (TEM)369
Mature leaves (third pair of leaves at the top of the plant) of the WT and GL were collected for TEM analysis.370
Avoiding the main vein, we cut the fresh tissue into 1–2-mm³ pieces and transferred them to 2.5% (v/v)371
glutaraldehyde for vacuum infiltration. Next, the pieces were pre-fixed in 2.5% glutaraldehyde for 24 h at 4°C,372
followed by 1% OsO4for 2 h. After dehydration, infiltration, and embedding, the pieces were sectioned using an373
EM UC6 ultramicrotome (Leica Microsystems GmbH, Wetzlar, Germany), and observed using a JEOL 1200374
transmission electron microscope (JEOL Ltd., Tokyo, Japan).375
Metabolite profiling
376
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Metabolites were extracted from WT, mutant leaves and the equivalent mixture of two materials taken as samples.
377
Samples (0.1 g) were analyzed by liquid chromatography electrospray ionization tandem mass spectroscopy
378
(LC-ESI-MS/MS) with SIMCA-P data-analysis software. Next, a principal component analysis (PCA), partial
379
least-squares discrimination analysis (PLS-DA), and an orthogonal partial least-squares discrimination analysis
380
(OPLS-DA) were performed to verify the reliability of the data. Fold-change (FC) (FC > 2 or FC < 0.5 and P <
381
0.05) and variable importance for projection (VIP) (VIP > 1 and P < 0.1) criteria were applied to identify
382
differential metabolites.
383
Small RNA sequencing
384
Library preparation and sequencing
385
Two clones of the same cultivars in both the WT and the GL libraries were used for the small RNA sequencing.
386
Small RNA libraries were constructed using the KAITAI-BIO Small RNA Library Prep Kit (KAITAI-BIO, China)
387
according to the manufacturer's instructions. The library preparations were sequenced on an Illumina HiSeq
388
platform.
389
Data analysis
390
The raw reads obtained were processed. Fastp software is used to filter Raw Data, mainly including removing
391
primer and connector sequences; low quality sequences at the end were removed (>20% bases <Q30, and N
392
base >10%); and those shorter than 18 nt and longer than 30 nt were also discarded.
393
Identification and annotation of the miRNAs
394
The derived reads were further screened against rRNA, tRNA, snRNA, snoRNA and other ncRNA as well as
395
repeat sequences by mapped to RNA database, which is Rfam (http://rfam.xfam.org/) and the remaining
396
unannotated reads were then mapped onto the Sapindus mukorossi reference genome.
397
Importantly and notably, there was not miRNA annotation in the K. paniculata full-length transcriptome.
398
Therefore, to identify known miRNAs, the mapped reads with the K. paniculata reference genome were mapped
399
onto the miRNAs of Sapindus mukorossi, which is the most evolutionarily close to K. paniculata.
400
The unaligned unique reads were further used for novel miRNA prediction using miRDeep-P2 (Kuang et al.,
401
2018) and newly updated criteria for plant miRNA criteria (Axtell and Meyers, 2018), referring to the method
402
described by Guo et al. (2020). Furthermore, the identified miRNAs were clustered into families based on
403
sequence similarity.
404
Differential expression analysis of miRNAs
405
To identify differentially expressed miRNAs (DEmiRs), edgeR was used. To filter out miRNAs with low
406
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expressions, those with |log2(FC)| ≥ 1.00 and a Benjamini-Hochberg FDR corrected P - value < 0.05 were
407
assigned as differentially expressed. MiRNAs related to chloroplast development, photosynthesis and pigment
408
metabolism pathway were screened from differentially expressed miRNAs, and analyzed in heat maps.
409
Target genes prediction
410
Potential miRNA target genes were predicted using the psRNA Target, RNAhybrid Target and miranda Target.
411
Target genes of miRNAs related to chloroplast development, photosynthesis and pigment metabolism pathway
412
were predicted and counted.
413
PacBio library preparation and sequencing414
To obtain integrated full-length transcriptome sequences of Koelreuteria, we pooled total RNA of the root, stem,
415
leaf, seed, and flower in equal quantities to construct sequencing libraries. Next, we used the SMARTer PCR
416
cDNA Synthesis Kit to synthesize full-length cDNA. After end repair, adaptor ligation, and index code addition,
417
PCR amplification was conducted. The polymerase-bound template was bound to MagBeads and sequencing was
418
performed on a PacBio Sequel platform to obtain polymerase reads.
419
By filtering out the adapter sequences and subreads < 1000 bp and the raw polymerase read fragment
420
sequences < 50 bp or sequence accuracy of < 0.80, we extracted the suitable subreads from the polymerase reads.
421
The reads of insert (RoIs) obtained by screening were classified into full-length non-chimeric (FLNC),
422
non-full-length (NFL), full-length-chimeric, and short (< 300 bp) reads depending on whether a 5ʹ-primer,
423
3ʹ-primer, or poly-A tail was detected. Finally, similar sequences among the FLNC sequences were clustered to
424
nonredundant isoforms after removing redundant high-quality consensus FLNC reads using CD-HIT program
425
with a threshold of 0.99 identity.
426
RNA-Seq library preparation, sequencing, and expression estimation427
Total RNA was isolated from the wild type and mutant with a Quick RNA Isolation Kit (Omega) and used for428
RNA-seq. We performed 1% agarose gel electrophoresis and used the Agilent 2100 Bioanalyzer to assess RNA429
integrity. RNA purity and concentration were measured using a NanoDrop 2000 spectrophotometer (Thermo430
Fisher). Library construction and RNA-seq were performed by HTHealth Biotechnology Co., Ltd. (Beijing,431
China). The libraries were subjected to next-generation sequencing on the Illumina HiSeq platform. Finally, we432
used in-house Perl scripts to process the low-quality data to obtain clean data.433
We combined the unigenes with the PacBio Iso-Seq data resulting in a final reference transcriptome. Isoform434
and unigene expression levels were quantified using RSEM software (https://github.com/deweylab/rsem), and435
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transcript lengths were normalized to FPKM values. The unigenes were mapped to five public databases: NCBI
436
nonredundant protein sequences (NR), Swiss-Prot, Gene Ontology (GO), evolutionary genealogy of genes:
437
Non-supervised Orthologous Groups (eggNOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG), using
438
BLAST software to obtain annotation information. We used DESeq software (Anders et al., 2010) to analyze gene
439
expression with |log2foldchange| > 1 and p < 0.05 as the screening criteria. Finally, the DEGs were subjected to
440
GO and KEGG analyses.
441
Quantitative RT-qPCR442
RNA-seq samples were subjected to RT-qPCR to assess the reliability of the transcriptome data. RNA extraction
443
and cDNA synthesis were conducted as above. The 2× SYBR Green qPCR Mix Kit (TaKaRa, Japan) and the
444
cDNA concentration and primers described above were used; the reaction volume was 25 µL and PCR was
445
conducted using the 7500 Real-Time PCR System (Applied Biosystems). Three independent biological replicates
446
per sample and three technical replicates per biological replicate were analyzed. The primer sequences are listed in
447
Supplementary Table S1. DEG expression was normalized by the 2−ΔΔCt method (Livak et al., 2001).
448
449
Supplemental data450
Supplemental Figure S1 Relative content of chlorophyll and chlorophyll intermediaries between normal green451
and mutant leaves. (a) Plant leaves in May. (b) Plant leaves in September.
452
Supplemental Figure S2 Score plot of metabolite profiles of J1-J3 (GL) and B1-B3 (WT).453
Supplemental Figure S3 Classification of differential metabolites
454
Supplemental Figure S4 KEGG enrichment of different metabolites455
Supplemental Figure S5 Full length transcriptome sequencing data. (a) ROI sequence length distribution. (b) Pie
456
chart of ROI classification. (c) ROI sequence quality distribution map. (d) Sequence length distribution after457
clustering
458
Supplemental Figure S6 KEGG classification of K. paniculata unigenes.459
Supplemental Figure S7 GO enrichment of K. paniculata unigenes.
460
Supplemental Figure S8 The difference expression results of metabolites and related transcripts461
Supplemental Figure S9 Distribution of transcription factors
462
Supplemental Figure S10 RT-qPCR analysis of the expression of fifteen DEGs at different developmental stages463
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17
between K. paniculata and K. paniculata ‘Jinye’. Asterisks indicate: (*) P⩽0.05, (**) P⩽0.01464
Supplemental Table S1 Primer sequence of RT-qPCR465
Supplemental Table S2 Classification of differential metabolites466
Supplemental Table S3 Statistical table of clustering sequence467
Supplemental Table S4 Summary of annotation results468
Supplemental Table S5 List of miRNAs related to chlorophyll metabolic pathway and their target genes in two469
samples470
Supplemental Table S6 List of miRNAs related to carotenoid metabolic pathway and their target genes in two471
samples472
Supplemental Table S7 List of miRNAs related to flavonoid synthesis pathway and their target genes in two473
samples474
475
Acknowledgements
476
This work was supported by the National Natural Science Foundation of China (31870652). We thank Mr. Wang477
Guodong for providing experimental materials. The English in this document has been checked by at least two478
professional editors, both native speakers of English. For a certificate, please see:479
http://www.textcheck.com/certificate/te8dE6480
481
Funding Support
482
This work was supported by the Natural Science Foundation of China (31870652), the Opening Foundation of483
Key Laboratory of Urban Agriculture (North China), Ministry of Agriculture, P. R. China (kf2018012)484
485
Competing interests:
486
The authors have declared that no competing interests exist.
487
488
Table 1 Full length transcriptome sequencing results. FL reads: full-length reads; FLNC reads: full-length489
non-chimeric reads.490
Table 2 Summary of reads results before and after processing.491
492
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18
Figure legends493
Figure 1 Chloroplast ultrastructure on K. paniculata and K. paniculate ‘Jinye’ in July. (a, b) The chloroplast494
ultrastructure of normal green leaves showed typical structure and distinct thylakoid membrane. (c, d) Abnormal495
chloroplast ultrastructures contained irregularly arranged vesicles in the mutant. Bars = 5μm (a, c), 1μm (b, d). CH496
chloroplast, CW cell wall, V vacuole, P plastoglobuli, T thylakoid grana, SG starch granule.497
Figure 2 Determination of pigment contents in K. paniculata and K. paniculata ‘jinye’. (a) anthocyanin contents498
of normal green and mutant leaves. (b) Carotenoid contents of normal green and mutant leaves. (c) The ratio of499
carotenoid to chlorophyll content of normal green and mutant leaves. (d) Relative content of chlorophyll and500
chlorophyll intermediaries between normal green and mutant leaves in July. Asterisks indicate: (*) P⩽0.05, (**)501
P⩽0.01502
Figure 3 Hierarchical clustering of metabolites in WT and GL. Intensity values were adjusted by log503
transformation and then normalized. Flavonoid with significant differences in abundance among differently504
colored leaves are indicated in red text.505
Figure 4 Differentially expressed miRNAs in the pigment metabolic pathway.506
Figure 5 Functional annotation of unigenes in leaf transcriptomes of K. paniculata among different samples. (a)507
Summary of the transcriptome DEGs. (b) Top 7 species distribution of K. paniculata unigenes. (c) KOG508
classification of K. paniculate unigenes. (d) GO classification of K. paniculata unigenes. (e) KEGG pathway509
enrichment of DEGs.510
Figure 6 Differentially expressed genes (a) Chlorophyll metabolic pathway (b) Carotenoid metabolic pathway (c)511
Flavonoid synthesis pathway512
Figure 7 The proposed pathway of mutant leaf coloration in K. paniculata513
Figure 8 Phenotypes of leaves of K. paniculata and K. paniculata ‘Jinye’ in July. (a, c, e) Phenotype of the normal514
green leaves. (b, d, f) Phenotype of the mutant leaves.515
516
References517
Agati G, Azzarello E, Pollastri S, Tattini M. (2012) Flavonoids as antioxidants in plants: location and functional518
significance. Plant Science 196:67-76.
519
Anders S, Huber W. (2010) Differential expression analysis for sequence count data. Genome Biology520
11(10):R106.
521
Axtell MJ and Meyers BC. (2018) Revisiting criteria for plant microRNA annotation in the era of big data. Plant522
Cell 30: 272–284
523
Cazzonelli C I, Pogson B J. (2010) Source to sink: regulation of carotenoid biosynthesis in plants. Trends in Plant524
.CC-BY-NC-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 19, 2022. ; https://doi.org/10.1101/2022.05.19.492690doi: bioRxiv preprint
19
Science 15:266-274.525
Chang Q S, Zhang L X, Chen L, Chen S M, Liu Z L, Fang W M, Chen F D. (2013) Characteristics of
526
Photosynthesis and Spectra Properties of Thylakoid Membranes in the Yellow-Green Leaf Mutant of527
Chrysanthemum. Scientia Silvae Sinicae 49(02):72-78.528
Du H, Wang N, Cui F, Li X, Xiao J, Xiong L. (2010) Characterization of the beta-carotene hydroxylase gene
529
DSM2 conferring drought and oxidative stress resistance by increasing xanthophylls and abscisic acid530
synthesis in rice. Plant Physiology 154:1304-1318.
531
Fraser P D, Enfissi E M, Halket J M, Truesdale M R, Yu D, Gerrish C. (2007) Manipulation of phytoene levels in532
tomato fruit: Effects on isoprenoids, plastids, and intermediary metabolism. Plant Cell 19 (10):3194-3211.
533
Fu C C, Han Y C, Fan Z Q, Chen J Y, Chen W X, Lu W J, Kuang J F. (2016) The Papaya transcription factor534
CpNAC1 modulates carotenoid biosynthesis through activating phytoene desaturase genes CpPDS2/4
535
during fruit ripening. Journal of Agricultural & Food Chemistry 64(27):5454-63.536
Gang H, Liu G, Chen S, Jiang J. (2019) Physiological and transcriptome analysis of a yellow-green leaf mutant in
537
Birch (Betula platyphylla × B. Pendula). Forests 10(2):120.538
Gang H, Li R, Zhao Y, Liu G, Chen S, Jiang J. (2019) Loss of GLK1 transcription factor function reveals new
539
insights in chlorophyll biosynthesis and chloroplast development. Journal of Experimental Botany540
70:3125-3138.
541
Guo Z, Kuang Z, Wang Y, Zhao Y, Tao Y, Cheng C, Yang J, Lu X, Hao C, Wang T, et al. (2020) PmiREN: a542
comprehensive encyclopedia of plant miRNAs. Nucleic Acids Res 48: D1114-D1121
543
Hao Z, Liu S, Hu L, Shi J. (2020) Transcriptome analysis and metabolic profiling reveal the key role of544
carotenoids in the petal coloration of Liriodendron tulipifera. Horticulture research 7:70.
545
Ji K, Kai W, Zhao B, Wang H, Guo Y, Leng P, et al. (2014) SlNCED1 and SlCYP707A2: key genes involved in546
ABA metabolism during tomato fruit ripening. Journal of Experimental Botany 65:5243-5255.
547
Kang B, Gu Q, Tian P, Xiao L, Cao H, Yang W. (2014) A chimeric transcript containing Psy1 and a potential548
mRNA is associated with yellow flesh color in tomato accession PI 114490. Planta 240:1011-1021.
549
Kim S C, Baek S H, Hong K N, Lee J W. (2017) Characterization of the complete chloroplast genome of550
Koelreuteria paniculata (Sapindaceae). Conservation Genetics Resources 10:69-72.
551
Klodawska K, Kis M, Malec P, Kovacs L, Gombos Z, Strzalka K. (2013) Morphological and physiological552
characterization of the delta-psaL mutant of cyanobacterium Synechocystis PCC6803. Polyhedron
553
18:289-297.554
Kuang Z, Wang Y, Li L, and Yang X. (2018) miRDeep-P2: accurate and fast analysis of the microRNA
555
transcriptome in plants. Bioinformatics 35: 2521–2522556
Li W, Yang S, Lu Z, He Z, Ye Y, Zhao B, Wang L, Jin B. (2018) Cytological, physiological, and transcriptomic
557
analyses of golden leaf coloration in Ginkgo biloba L. Horticulture research 5:12.558
Li Y, Zhang D Q, Zhang S Q, Lou Y F, An X M, Jiang Z H, Gao Z M. (2022) Transcriptome and miRNAome
559
analysis reveals components regulating tissue differentiation of bamboo shoots. Plant physiology560
.CC-BY-NC-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 19, 2022. ; https://doi.org/10.1101/2022.05.19.492690doi: bioRxiv preprint
20
00:1–17.561
Li Y, Zhang Z, Wang P, Wang S, Ma L, Li L, Yang R, Ma Y, Wang Q. (2015) Comprehensive transcriptome562
analysis discovers novel candidate genes related to leaf color in a Lagerstroemia indica yellow leaf563
mutant. Genes & Genomics 37: 851-863.564
Livak K J, Schmittgen T D. (2001) Analysis of relative gene expression data using real-time quantitative PCR and565
the 2-ΔΔC(T) method. Methods 25:402-408.566
Li B J, Zheng B Q, Wang J Y, Qiao H J, Liu Z J, Wang Y. (2020) New insight into the molecular mechanism of567
colour differentiation among floral segments in orchids. Communication Biology 3(1):89.568
Li F, Vallabhaneni R, Wurtzel E T. (2008) PSY3, a new member of the phytoene synthase gene family conserved569
in the Poaceae and regulator of abiotic stress-induced root carotenogenesis. Plant Physiology570
146:1333-1345.571
Lyu Y Z, Dong X Y, Huang L B, Huang L S. (2017) De novo assembly of Koelreuteria transcriptome and analysis572
of major gene related to leaf etiolation. South African Journal of Botany 113:355-361.573
Liu Y Y, Chen X R, Wang J P, Eliphaz N, Sun M Q, Li Y H. (2019) Transcriptomic analysis reveals flavonoid
574
biosynthesis of Syringa oblata Lindl. in response to different light intensity. BMC Plant Biology575
11;19(1):487.576
Maiwald D, Dietzmann A, Jahns P, Pesaresi P, Joliot P, Joliot A, Levin JZ, Salamini F, Leister D. (2003)
577
Knock-out of the genes coding for the Rieske protein and the ATP-synthase delta-subunit of Arabidopsis.578
Effects on photosynthesis, thylakoid protein composition, and nuclear chloroplast gene expression. Plant
579
Physiology 133(1):191-202.580
Matringe M, Camadro J M, Block M A, Joyard J, Scalla R, Labbe P, Douce R. (1992) Localization within
581
chloroplasts of protoporphyrinogen oxidase, the target enzyme for diphenylether-like herbicides. Journal582
of Biological Chemistry Mar 5;267(7):4646-51.
583
Ma J, Li J, Zhao J, Hui Z, Han Y. (2014) Inactivation of a gene encoding carotenoid cleavage dioxygenase (CCD4)584
leads to carotenoid-based yellow coloration of fruit flesh and leaf midvein in peach. Plant Molecular
585
Biology Reporter 32: 246-257.586
Mohd I, Hawa J. (2013) Abscisic acid induced changes in production of primary and secondary metabolites,
587
photosynthetic capacity, antioxidant capability, antioxidant enzymes and lipoxygenase inhibitory activity588
of orthosiphon stamineus benth. Molecules 18(7):7957-7976.
589
Nakajima S, Ito H, Tanaka R, Tanaka A. (2012) Chlorophyll b reductase plays an essential role in maturation and
590
storability of Arabidopsis seeds. Plant Physiology 160:261.591
Schwartz S H. (2001) Characterization of a novel carotenoid cleavage dioxygenase from plants. Journal of
592
Biological Chemistry 276:25208-25211.593
Shen J S. (2019) Molecular mechanism of yellow leaf formation in yellow-leafed Forsythia. Beijing Forestry594
University595
Song L, Ma Q, Zou Z, Sun K, Yao Y, Tao J, Kaleri NA, Li X. (2017) Molecular Link between Leaf Coloration and
596
.CC-BY-NC-ND 4.0 International licensemade available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is
The copyright holder for this preprintthis version posted May 19, 2022. ; https://doi.org/10.1101/2022.05.19.492690doi: bioRxiv preprint
21
Gene Expression of Flavonoid and Carotenoid Biosynthesis in Camellia sinensis Cultivar 'Huangjinya'.597
Frontiers in Plant Science 8:803.598
Tanaka Y, Sasaki N, Ohmiya A. (2008) Biosynthesis of plant pigments: anthocyanins, betalains and carotenoids.599
Plant Journal 54:733-749.
600
Tian D, Zhu F, Yan W, Xi F, Peng C. (2009) Heavy metal accumulation by panicled goldenrain tree (Koelreuteria601
paniculata) and common elaeocarpus (Elaeocarpus decipens) in abandoned mine soils in southern China.
602
Journal of Environmental Sciences 21:340-345.603
Veit S, Takeda K, Tsunoyama Y, et al. (2012) Structure of a thermophilic cyanobacterial b6f-type Rieske protein.604
Acta Crystallographica 68(10):1400-1408.
605
Wen L, Sha T, Zhang S, Shan J, Diao X. (2016) Gene mapping and functional analysis of the novel leaf color gene
606
SiYGL1 in foxtail millet [Setaria italica (L.) P. Beauv]. Physiologia Plantarum 157:24-37.607
Wang F, Huang Y, Wu W, Zhu C, Zhang R, Chen J, Zeng J. (2020) Metabolomics analysis of the peels of different
608
colored citrus fruits (Citrus reticulata cv. 'Shatangju') during the maturation period based on609
UHPLC-QQQ-MS. Molecules 25(2):396.
610
Winkel-Shirley B. (2001) Flavonoid biosynthesis. A colorful model for genetics, biochemistry, cell biology, and611
biotechnology. Plant Physiology 126:485-493.
612
Yang H Y, Xia X W, Fang W, Fu Y, An M M, Zhou M B. (2015) Identification of genes involved in spontaneous613
leaf color variation in Pseudosasa japonica. Genetics and Molecular Research 14:11827-11840.
614
Yang L P, Zhu J, Wang P, Zeng J, Tan R, Yang Y Z, Liu Z M. (2018) Effect of Cd on growth, physiological615
response, Cd subcellular distribution and chemical forms of Koelreuteria paniculata. Ecotoxicology and
616
Environmental Safety 160:10-18.617
Zhang S, Zuo L, Zhang J, et al. (2017) Transcriptome analysis of Ulmus pumila 'Jinye' responses to different618
shading involved in chlorophyll metabolism. Tree Genetics & Genomes 13(3):64.
619
Zhang J, Guo S, Ren Y, He H, Liu F, Xu Y, et al. (2017) High-level expression of a novel chromoplast phosphate
620
transporter ClPHT4;2 is required for flesh color development in watermelon. New phytologist621
213(3):1208-1221.
622
Zhang S, Wu X, Cui J, Zhang F. (2019) Physiological and transcriptomic analysis of yellow leaf coloration in623
Populus deltoides Marsh. PLoS One 14(5):e0216879.
624
Zhang Q, Wang L, Liu Z, Zhao Z, Zhao J, Wang Z, Zhou G, Liu P, Liu M. (2020) Transcriptome and metabolome625
profiling unveil the mechanisms of Ziziphus jujuba Mill. peel coloration. Food Chemistry 312:125903.
626
Zhou W, Niu Y, Ding X, Zhao S, Li Y, Fan G, Zhang S, Liao K. (2020) Analysis of carotenoid content and627
diversity in apricots (Prunus armeniaca L.) grown in China. Food Chemistry 330:127223.
628
629
630
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The copyright holder for this preprintthis version posted May 19, 2022. ; https://doi.org/10.1101/2022.05.19.492690doi: bioRxiv preprint
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Table 1 Full length transcriptome sequencing results
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Reads
of Insert
Mean Length
of Insert
5' primer
reads
3' primer
reads
Poly-A
reads
FL
reads
FLNC
reads
Average FLNC
read length
FLNC
%
355606
2011
350877
351025
348207
342054
333598
1862
93.81
FL reads: full-length reads;
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FLNC reads: full-length non-chimeric reads.
635
636
Table 2 Summary of reads results before and after processing
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Sample
Raw Data
Valid date
Valid ratio
unigenes
Read
Base
Read
Base
WT
40299129
6044869400
37326844
5599026700
93%
79393
GL
39931987
5989798100
37007752
5551162800
93%
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Fig.1 Chloroplast ultrastructure on K. paniculata and K. paniculate ‘Jinye’ in July. (a, b) The chloroplast ultrastructure of normal639
green leaves showed typical structure and distinct thylakoid membrane. (c, d) Abnormal chloroplast ultrastructures contained
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irregularly arranged vesicles in the mutant. Bars = 5μm (a, c), 1μm (b, d). CH chloroplast, CW cell wall, V vacuole, P plastoglobuli,
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T thylakoid grana, SG starch granule.
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643
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Fig.2 Determination of pigment contents in K. paniculata and K. paniculata ‘jinye’. (a) anthocyanin contents of normal green and645
mutant leaves. (b) Carotenoid contents of normal green and mutant leaves. (c) The ratio of carotenoid to chlorophyll content of646
normal green and mutant leaves. (d) Relative content of chlorophyll and chlorophyll intermediaries between normal green and mutant647
leaves in July. Asterisks indicate: (*) P⩽0.05, (**) P⩽0.01648
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Fig.3 Hierarchical clustering of metabolites in WT and GL. Intensity values were adjusted by log transformation and then normalized.
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Flavonoid with significant differences in abundance among differently colored leaves are indicated in red text.651
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Fig.4 Differentially expressed miRNAs in the pigment metabolic pathway.
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25
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Fig.5 Functional annotation of unigenes in leaf transcriptomes of K. paniculata among different samples. (a) Summary of the
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transcriptome DEGs. (b) Top 7 species distribution of K. paniculata unigenes. (c) KOG classification of K. paniculate unigenes. (d)658
GO classification of K. paniculata unigenes. (e) KEGG pathway enrichment of DEGs.659
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Fig.6 Differentially expressed genes (a) Chlorophyll metabolic pathway (b) Carotenoid metabolic pathway (c) Flavonoid synthesis
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pathway663
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27
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Fig. 7 The proposed pathway of mutant leaf coloration in K. paniculata
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Fig.8 Phenotypes of leaves of K. paniculata and K. paniculata ‘Jinye’ in July. (a, c, e) Phenotype of the normal green leaves. (b, d, f)
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Phenotype of the mutant leaves.669
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