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Functional Structure/Activity Relationships
Comparative Transcriptomic Analysis Reveals Regulatory Mechanisms of Theanine Synthesis in Tea (Camellia sinensis) and Oil Tea (Camellia. oleifera) Plants Yuling Tai, chengcheng Ling, huanhuan Wang, lin yang, guangbiao she, chengxiang wang, shuwei yu, Wei Chen, Chun Liu, and Xiaochun Wan J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.9b02295 • Publication Date (Web): 22 Aug 2019 Downloaded from pubs.acs.org on August 22, 2019
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Journal of Agricultural and Food Chemistry
Comparative Transcriptomic Analysis Reveals Regulatory Mechanisms of Theanine Synthesis in Tea (Camellia sinensis) and Oil Tea (Camellia oleifera) Plants Yuling Tai,†,‡ Chengcheng Ling,† Huanhuan Wang,† Lin Yang,† Guangbiao She,‡ Chengxiang Wang,† Shuwei Yu,‡ Wei Chen,† Chun Liu, *,†,§ Xianchun Wan, *,‡ † School of Life Science, Anhui Agricultural University, Hefei 230036, China
‡ State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
§ BGI Genomics, BGI-Shenzhen, Shenzhen, China
Corresponding author:
(Chun Liu) E-mail:
[email protected] (Xiaochun Wan) E-mail:
[email protected] 1
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ABSTRACT: Tea provides a rich taste and has healthy properties due to its variety of
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bioactive compounds, such as theanine, catechins and caffeine. Theanine is the most
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abundant free amino acid (40%−70%) in tea leaves. Key genes related to theanine
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biosynthesis have been studied, but relatively little is known about the regulatory
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mechanisms of theanine accumulation in tea leaves. Herein, we analyzed theanine
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content in tea (Camellia sinensis) and oil tea (Camellia oleifera), and found it to be
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higher in roots than other tissues in both species. The theanine content was significantly
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higher in tea than oil tea. To explore the regulatory mechanisms of theanine
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accumulation, we identified genes involved in theanine biosynthesis by RNA-Seq
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analysis, and compared theanine-related modules. Moreover, we cloned theanine
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synthase (TS) promoters from tea and oil tea plants, and found that a difference in TS
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expression and cis-acting elements may explain the difference in theanine accumulation
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between the two species. These data provide an important resource for regulatory
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mechanisms of theanine accumulation in tea plants.
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KEYWORDS: Tea, oil tea, theanine biosynthesis, transcriptomics, regulatory
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mechanism, RNA-seq, CsTS promoter, CoTS promoter, transcription factors
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INTRODUCTION
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Consumed since 3,000 BC, tea is one of the most important beverage crops worldwide,
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and offers important health benefits due to its numerous bioactive compounds including
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catechins, theanine, caffeine, vitamins and volatile oils1-3. These compounds also
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contribute to the pleasant aroma and appealing taste4-6. Tea is made from the leaves of tea
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plants (Camellia sinensis) belonging to genus Camellia in the Theaceae family of
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angiosperms. The Camellia genus contains several other economically important species,
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including the traditional oil tea plants (Camellia oleifera) that produce high-quality edible
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seed oil 7. Oil tea plants are genetically closely related to tea plants, and are grown only
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in China8-11.
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Theanine (γ-glutamylethylamide) is one of the major amino acid components in tea
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beverages, accounting for 1−2% dry weight 40−70% of total free amino acids in tea
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leaves
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plants, and has been detected in trace amounts in C. oleifera, C. japonica and C.
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sasanqua, as well as the fungus Xerocomus badius
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compounds in tea plants, theanine contributes to the umami and sweet tastes. In addition,
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recent studies suggest that theanine has a variety of health functions including controlling
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hypertension, lowering blood pressure, reducing anxiety and inhibiting tumor growth 15 16
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blood-brain barrier 18, 19.
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. Theanine is a unique non-protein amino acid found almost exclusively in tea
13, 14
. As one of the characteristic
, and it can affect the mammalian central nervous system by passing through the
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Many researchers have investigated the biosynthesis of theanine and found that it is
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synthesized in tea plant roots and transported to aerial parts, especially tender shoots 20, 21.
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The key enzymes related to the theanine biosynthesis pathway are glutamine synthetase
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(GS), theanine synthetase (TS), glutamate synthase (GOGAT) and glutamate
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dehydrogenase (GDH)
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performed in the 1960s 23, but more recent research
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and regulation mechanisms related to the theanine biosynthesis pathway. However, little
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is currently known about the properties of the theanine synthesis-related enzymes in tea
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plants. It is interesting that only tea plants appear to synthesize and accumulate large
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quantities of theanine.
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. Studies on theanine biosynthesis in tea plants were first 24, 25
has identified the key enzymes
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Weighted correlation network analysis (WGCNA) is a popular method for gene
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co-expression network analysis, and this correlation-based approach facilitates
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visualization of co-expression networks derived from transcriptomic data
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technique has been successfully utilized to compare gene-expression patterns in
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domesticated and wild tomato
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networks for tea and oil tea based on transcriptomic data, and identified candidate genes
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including TS (hereafter referred to as TSI) in theanine-related modules. TS is one of the
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key enzymes and catalyzes the last step in theanine synthesis. Recently, Wei et al. (2018)
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identified candidate gene CsTSI in the tea plant genome
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protein product possessed theanine synthetase activity. Herein, we cloned and analyzed
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. This
. In the present study, we constructed co-expression
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, and demonstrated that the
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the C. sinensis and C. oleifera TS promoters (CsTSI and CoTSI) associated with the
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theanine-related modules, and predicted the transcription factor (TF) binding sites
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therein. Our findings provide useful data for theanine research, and illuminate the
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mechanisms regulating the accumulation and biosynthesis of theanine. Our transcriptome
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data will serve as an important resource for future functional genomics and breeding of
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tea plants.
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MATERIALS AND METHODS
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Plant Materials
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Cuttings of 6-year-old C. oleifera and C. sinensis plants were used, and plants were
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grown in natural conditions at the De Chang Fabrication Base in Anhui Province, China
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(Shucheng, latitude 31.3° N, longitude 117.2° E, above sea level). All plants were
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fertilized and watered equally. Plants were grown in an experimental field with 150 cm
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between rows and 40 cm between plants within a row, and the soil was acidic (pH 5.6).
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Three independent biological replicates were performed for each sample, and each
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sample was collected from 15 randomly selected tea and oil tea plants, respectively. All
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samples were frozen in liquid nitrogen immediately after harvesting, and stored at −80°C
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until RNA isolation. Bud (CO-B, CS-B), first young leaf (CO-L1, CS-L1), second young
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leaf (CO-L2, CS-L2), mature leaf (CO-ML, CS-ML), stem (CO-S, CS-S), flower
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(CO-FL, CS-FL), fruit (CO-FR, CS-FR) in June, and root (CO-R, CS-R) tissues
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comprised the eight tissue samples studied. All necessary permits were obtained from
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Wenyong Zhan, the director of the De Chang Fabrication Base.
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Extraction and HPLC Determination of Theanine
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Theanine was extracted from samples using hot water as described previously
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some modifications. Briefly, 0.20 g of freeze-dried tea leaves were extracted with
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deionized water for 20 min in a boiling water bath and centrifuged at 6,000 rpm for 10
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min. Residues were re-extracted once as described above, supernatants were combined
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and diluted with water to 10 mL, and filtered through a 0.22-μm membrane before HPLC
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determination. Each sample was analyzed using three independent biological replicates.
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Theanine content was measured using a Waters 600E series HPLC system (Waters, USA)
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at a detection wavelength of 199 nm, and the injection approach and mobile phase ratio
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were as described previously 13. The theanine standard used in this study was purchased
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from Shanghai Winherb Medical Technology, Ltd., China. Theanine content is reported
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as mean ± standard deviation (SD) from three independent biological replicates.
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RNA Extraction, Library Construction, and Sequencing
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Total RNA was extracted separately from each tissue using a modified CTAB (hexadecyl
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trimethyl ammonium bromide) method as described previously 30, and each sample was
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extracted in triplicate. The quality and quantity of total RNA were characterized using
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1.2% agarose gel electrophoresis (AGE) and examined with a NanoDrop 2000 instrument
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(USA). RNA integrity (RIN) was determined using an Agilent Bioanalyzer 2100
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(Agilent, USA), and RNA samples with an A260/A280 ratio >1.8, A260/A230 ratio >1.8,
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and RIN >8 were considered suitable for library construction. Equal amounts of RNA
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with
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from three independent biological replicates were pooled prior to cDNA library
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preparation. Library construction (including mRNA enrichment, cDNA synthesis,
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fragment interruption, adapter addition, size selection, PCR amplification, and
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transcriptomic sequencing) was performed by staff at the Beijing Genome Institute (BGI;
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Shenzhen, China). Briefly, mRNA was enriched from total RNA using magnetic beads
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with Oligo (dT), and cleaved into short fragments. These fragments were synthesized into
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first-strand cDNA with random primers (TaKaRa, Japan), and double-stranded cDNAs
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were then synthesized and repaired using T4 DNA ligase Klenow DNA polymerase and
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T4 polynucleotide kinase (NEB, USA). Products were enriched by PCR to generate the
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cDNA library after ligation of adapters. The cDNA library was examined after quality
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control using an Agilent 2100 Bioanalyzer, and 90 bp paired-end reads were obtained for
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qualified library construction using an Illumina HiSeq 2500 sequencer 31.
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De Novo Transcriptome Assembly and Open Reading Frame Identification
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Sequencing data (raw reads) were firstly filtered by removing low-quality reads (base
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quality 5%). The remaining high-quality reads (clean reads) were de novo assembled using
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Trinity 32 with parameters ‘min_kmer_cov = 7’ and ‘min_glue = 3’. The resulting contigs
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were clustering and redundancy was removed using TGICL
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Non-redundant
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(https://transdecoder.github.io) was employed to identify candidate coding regions.
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High-quality ORF datasets were obtained by removing sequence redundancy using
contigs
were
considered
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with default parameters.
unigenes.
Transdecoder
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CD-HIT (version 4.6.8) 34. The completeness of unigenes was evaluated using the Busco
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(version 3.0.2)
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C. sinensis transcriptomic data (SRR1928149 and SRP056466) were used for comparison
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with the C. oleifera data (PRJNA274203).
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Functional Annotation and Classification
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Unigenes were classified into functional categories using various databases and tools.
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Unigenes and ORFs were aligned against NR (NCBI non-redundant protein sequences,
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SwissProt36 (a manually annotated and reviewed protein sequence database), KOG
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(Clusters of Orthologous Groups of proteins) and KEGG databases with a threshold
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E-value of 10-5. GO (Gene ontology) annotation was carried out using the Blast 2 GO
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program (version 2.3.4) based on BLAST search results against the NR database. Protein
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domains and important sites were predicted by InterPro (http://www.ebi.ac.uk/interpro/).
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TF analysis was carried out by iTAK
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rules were mainly summarized from PlnTFDB
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PlantTFcat 40 and AtTFDB 41 provided supporting evidence.
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Phylogenetic Analysis Evolutionary Relationships between TSs and GSs
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We retrieved the sequences identified from the Wei et al.
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and GS from C. sinensis var. assamica, C. oleifera and Elaeis guineensis. The
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phylogenetic tree was constructed and analyzed by MEGA X 42. Sequences of TS and GS
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from C.sinensis cv. Shuchazao C. sinensis var. assamica, C. oleifera, A. thaliana, P.
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vulgaris, G. max, Z. mays, T. aestivum, O. sativa, M. truncatula, E. guineensis were
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and Embryophyta databases, and the data used for subsequent analysis.
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using the standalone version (1.7a), consensus 38
and PlantTFDB
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, and families from
and added sequences of TS
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aligned
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maximum-likelihood method with five hundreds of bootstrap replicates
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phylogenetic tree was visualized by ITOL (https://itol.embl.de/index.shtml). A
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phylogenetic tree for AMT (ammonium transporter) was constructed using the same
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method.
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Identification of DEGs and Construction of Co-Expression Networks
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Unigene expression levels were estimated and normalized using the fragments per
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kilobase of exon model per million mapped reads (FPKM) method. We calculated DEGs
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based on the method described in Love et al.
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from RNA-seq reads, then estimated variance-mean dependence in count data and tested
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for differential expression based on a model using the negative binomial distribution.
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Genes with absolute log 2 ratio values ≥1 and a false discovery rate (FDR)
