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Article

Comparative Transcriptome Analysis Reveals a Tissue-Specific Pathway Involved in Nitrogen Utilization Between Genotypes with Different Nitrogen Use Efficiencies in Tea Plants (Camellia sinensis)

1
China Tea (Beijing) Chain Co., Ltd., Beijing 100020, China
2
College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
3
Rural Revitalization and Public Cultural Center of Jufeng, Rizhao 276812, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(12), 2824; https://doi.org/10.3390/agronomy14122824
Submission received: 26 October 2024 / Revised: 18 November 2024 / Accepted: 22 November 2024 / Published: 27 November 2024
(This article belongs to the Section Horticultural and Floricultural Crops)
Figure 1
<p>Comparison of nitrogen use efficiency among different tea cultivars. (<b>a</b>) N<sub>dff</sub> in different tissues; (<b>b</b>) <sup>15</sup>N accumulation in different tissues; (<b>c</b>) NUEs among different tea cultivars. The error bars indicate the standard deviations and the values corresponding to the mean ± standard deviation (SD) of three independent biological replicates. Different letters indicate significant difference (<span class="html-italic">p</span> &lt; 0.05).</p> ">
Figure 2
<p>Functional annotation classification for unigenes. (<b>a</b>) GO enrichment analysis for unigenes; (<b>b</b>) KOG enrichment analysis for unigenes; (<b>c</b>) KEGG enrichment analysis for unigenes. The red rectangle means the most enriched category.</p> ">
Figure 3
<p>Enrichment of DEGs for the top 20 KEGG pathways in different tissues. (<b>a</b>) Roots; (<b>b</b>) stems; (<b>c</b>) leaves; and (<b>d</b>) new shoots. The red dotted rectangles means the most significantly encirched pathways.</p> ">
Figure 4
<p>Expression profiles of DEGs involved in nitrogen uptake and transport in different tissues between the two cultivars. (<b>a</b>) Ammonium and nitrate transporter; (<b>b</b>) amino acid transporter.</p> ">
Figure 5
<p>Expression profiles of DEGs involved in photosynthesis in different tissues between the two cultivars. (<b>a</b>) <span class="html-italic">LHCB</span>; (<b>b</b>) <span class="html-italic">LHCB</span> in tea leaves.</p> ">
Figure 6
<p>Expression profiles of DEGs involved in cytokinin metabolism in different tissues between the two cultivars.</p> ">
Versions Notes

Abstract

:
Nitrogen (N) is a key nutrient which affects plant development and quality formation for tea plants. Notable genetic variation in nitrogen use efficiency (NUE) has been reported among different genotypes of Camellia sinensis. However, the molecular mechanisms underlying these differences have not been illuminated. In this study, a 15N tracing method was used to compare nitrogen use efficiency among six genotypes. The results show that there were significant differences in the NUEs among these genotypes. Among them, TC12 had the highest NUE, while LJCY had the lowest NUE. Transcriptome analysis between these two cultivars showed that differentially expressed genes (DEGs) were significantly enriched in photosynthesis—antenna proteins and zeatin biosynthesis in mature leaves and new shoots, respectively. TC12 had higher expression levels of AMT1.2, NRT2.4, and NRT3.2 in the roots, AAP6 and AAP7 in the stems and shoots, and LHC in the mature leaves than LJCY. The expression of ZOG1 and CKX, which are involved in zeatin biosynthesis, was down-regulated in the shoots of TC12 compared with LJCY. These findings will contribute to insights into the molecular mechanism of nitrogen utilization and the identified candidate genes provide a genetic resource for improving N use efficiency in tea plants.

1. Introduction

Nitrogen (N) is an essential nutrient element for tea plants. The process of picking tea leaves and pruning multiple times each year results in significant nitrogen consumption. More importantly, nitrogen also plays a vital role in determining the quality of tea by regulating the biosynthesis of catechins, free amino acids, caffeine, and aroma compounds in tea leaves [1,2,3]. In China, the average annual N inputs reach 300–450 kg·hm−2 to meet the demand of tea plants; excessive N fertilization has been reported to affect over 30% of the tea plantation area [4]. Excessive application of N not only increases cultivation costs, but also induces soil acidification [5], greenhouse gas emissions [6], and water eutrophication [7]. To address these issues effectively, breeding cultivars with a high nitrogen-use efficiency (NUE) is considered to be a promising approach. Therefore, it is crucial to clarify the mechanism underlying differences in nitrogen uptake, assimilation, and transport.
Nitrogen use efficiency is a complex trait that depends on various physiological and biochemical processes occurring in different tissues [8]. Inorganic N primarily enters root cells through ammonium and nitrate transporters via soil absorption [9,10]. Following root uptake, ammonium is metabolized to glutamate and glutamine by the Glutamine synthetase–Glutamate synthetase (GS-GOGAT) cycle either within roots or leaves [11,12]. The assimilated amino acids are then allocated to sink tissues with assistance from amino acid transporters [13]. In addition, these nitrogen metabolism processes can be influenced by other pathways such as photosynthesis in the leaves [14]. It has been observed that the tissue-specific pathways involved in nitrogen utilization exhibit diversity. For instance, Lin et al. (2023) found that DEGs were enriched in photosynthetic pathways in the leaves, while they were enriched in sugar and hormone-related pathways in the roots [15]. The tissue-specific pathway which is involved in nitrogen utilization in tea plants has not yet been clarified.
In tea plants, numerous attempts have been made to identity the key pathways and genes involved in N uptake, transport, and assimilation. Most of them have investigated the molecular response to different nitrogen concentrations or forms using transcriptome analysis. Xu et al. (2022) suggested that the flavonoid-related pathway, which includes the WRKY, MYB, and bHLH transcription factors, plays vital roles in tea roots’ response to N deprivation based on transcriptome analysis [16]. RNA-seq analysis revealed notable changes in the expression level of genes involved in secondary metabolite biosynthesis in response to ammonium or nitrate [17,18,19,20]. However, few studies have focused on elucidating the molecular mechanism underlying genotype differences in nitrogen utilization. Only Ruan et al. (2022) investigated global transcriptional changes between low-N-tolerant and intolerant tea genotypes during recovery from nitrogen deficiency [21]. The molecular mechanism responsible for genotypic differences in these complex processes has not been clarified in tea plants.
In the present study, a 15N tracing method was used to compare the NUEs of different tea genotypes. To gain a comprehensive understanding of the potential molecular mechanisms underlying NUE variations, we analyzed gene expression profiles across different tissues between two genotypes with contrasting NUEs. This study will provide novel insight into the diverse NUEs observed among tea genotypes, and the identified candidate pathway in different tissues can be targeted for improving NUE.

2. Materials and Methods

2.1. Plant Materials and Samples Collected

One-year-old cutting seedlings of six cultivars, including ‘Longjingchangye’ (LJCY), ‘Baiye 1’ (BY1), ‘ZhongHuang 3’ (ZH3), ‘Taicha12’ (TC12), ‘Meizhan’(MZ), and ‘Huangjinya’ (HJY), were used in this study. A total of 32 visually uniformly sized seedlings from each cultivar were cultured in a plastic pot filled with a mixture of soil, vermiculite, and sand mix as one treatment. Each treatment was repeated three times. After two weeks of pre-cultivation, a 15N label experiment was conducted using urea enriched with 5.25% 15N (purchased from Shanghai Research Institute of Chemical Industry Co. Ltd., Shanghai, China). An amount of 1 g of urea was applied to the soil surface and thoroughly mixed with the soil. The seedlings were then subjected to a light exposure of 12 h at 25 °C followed by darkness for another 12 h at 18 °C, maintaining photon flux densities of 300 μm·m−2·s−1 and the humidity level at approximately 75%.
After a duration of 30 days, the roots, mature leaves, stems, and new shoots from each cultivar were harvested separately. The samples were separated into two parts: one part was immediately frozen in liquid nitrogen and stored at −80 °C in an ultra-refrigerator for transcriptome analysis, the other part was dried in an electric oven at 65 °C to a constant weight for 15N determination.

2.2. Determination of Biomass, 15N Abundance, and Total N Content

The dried samples were weighed for the determination of biomass. The total N and 15N abundance were determined in an elemental analyzer (Carlo Erba, Milano, Italy) coupled to an isotope ratio mass spectrometer (Finigan Corp., Bremen, Germany). The 15N abundance was calculated as an atom percentage according to the following Equations (1) and (2).
N derived from fertilizer (Ndff, %) = (15N excess in plant)/(15N excess in fertilizer) × 100, where 15Nexcess (%) = 15N abundance (%) − natural 15N abundance (0.3660%)
15N amount in different tissues = Ndff × total N content × biomass (DW)

2.3. RNA Extraction, Sequencing, and Annotation

Total RNA from various tissues of two cultivars was extracted using an RNAprep Pure Plant Kit (Tiangen, Beijing, China) according to the manufacturer’s instructions. The integrity, purity, and concentration of the RNA samples were assessed using a NanoDrop ND-1000 spectrophotometer (NanoDrop, Waltham, MA, USA) and 1% agarose gels. Then, 24 libraries were constructed and subjected to sequencing. High-quality reads were obtained by filtering out reads containing adaptors or ploy-N (N > 5%) as well as low-quality reads from raw data using Cutadapt (V 1.9.1). The clean reads from all the samples were mapped to the genome of the tea plant using HISAT2 software (V 2.1.1). The quality control analysis of the sequencing data was performed with FastQC (V 0.11.9). Gene annotation was conducted through BLASTx, searched against the NCBI, Swiss-port, KEGG, and KOG/COG databases. Gene expression levels were calculated as FRKM values. Differential expression analysis was conducted using DESeq2 (V 1.40.0) with a threshold set at a fold change ≥ 1.5 and a false discovery rate < 0.01.

2.4. Quantitative Real-Time PCR Analysis (qRT-PCR)

Purified RNA was reverse transcribed using the PrimeScriptTM 1st Strand cDNA Synthesis Kit (Takara, Dalian, China). Primer Premier 5.0 was used to design the primers, and the primer sequences are listed in Table S1. For quantitative real-time PCR, a total volume of 20 µL included 0.2 µM primers, 50 ng cDNA, and 10 µL Cycler 480 SYBR Green I Master Reagent (Roche, Mannheim, Germany). The qRT-PCR was performed on a Roche LightCycler 480 (Roche Diagnostics, Mannheim, Germany). Each experiment was performed with three replicates. The reference gene GAPDH was utilized to quantify the relative expression level using the 2−ΔΔCt method.

2.5. Statistical Analysis

Analysis of variance (ANOVA) was performed by SPSS 20.0 (SPSS Inc., Chicago, IL, USA). To compare the genotypic difference, experimental data were subjected to one-way ANOVA supplemented by Duncan’s multiple range test. The differences were considered statistically significant when the p-value < 0.05.

3. Results

3.1. Comparison of NUEs Among Different Tea Cultivars

To compare the NUEs among different tea cultivars, 15N was applied to six tea cultivars and its measured distribution was in various tissues after thirty days. The new shoots exhibited higher Ndff compared to the other three tissues examined. The Ndff of mature leaves were lowest in LJCY, BY1, and MZ, and the Ndff of the stems were lowest in ZH3, TC12, and HJY (Figure 1a). Analysis of 15N distribution across different tissues revealed that TC12 accumulated the highest 15N amount, while LJCY had the lowest accumulation in both new shoots and mature leaves (Figure 1b). The trend observed in 15N accumulation within new shoots and mature leaves corresponded with whole-plant accumulation patterns. Ultimately, significant differences were found in NUEs among these cultivars, with TC12 exhibiting the highest NUE, followed by MZ, and LJCY had the lowest NUE (Figure 1c).

3.2. Global Expression Analysis of the Two Cultivars

In order to elucidate the mechanism responsible for differences in nitrogen utilization between different cultivars, transcriptome analysis was conducted on four tissues from the high-NUE cultivar (TC12) and the low-NUE cultivar (LJCY). At least 5.96 Gb of clean bases were obtained from each sample with a GC content of 43.98–47.08% and a Q30 of 91.53–94.62%. More than 81.23% of the sequences were successfully mapped onto the reference genome sequences (Table S2). To predict and analyze the function of these unigenes, they were aligned against seven databases. In total, 54,649 unigenes were annotated in at least one database (Table S3).
Gene ontology (GO) enrichment analysis showed that most genes were enriched in metabolic process and cellular process (Figure 2a). In total, 31,041 unigenes were assigned to 26 KOG clusters, with amino acid transport and metabolism being the most enriched category (Figure 2b). Furthermore, KEGG enrichment analysis demonstrated that these unigenes were significantly enriched in amino acid metabolism, carbohydrate metabolism, and lipid metabolism, suggesting their crucial roles in tea plant nitrogen utilization (Figure 2c)
The DEGs between the two cultivars were identified across various tissues. Totals of 15,794 (7918 up-regulated and 7856 down-regulated), 15,900 (8371 up-regulated and 7529 down-regulated), 18,356 (8421 up-regulated and 9935 down-regulated) and 15,900 (8371 up-regulated and 7529 down-regulated) DEGs were obtained from the roots, stems, leaves, and new shoots in TC12 compared with LJCY (Figure S1). These genes were searched against KEGG to explore their potential functions related to nitrogen utilization. The results indicated significant enrichment of these DEGs within different pathways across distinct tissues. DEGs were significantly enriched in the “Glutathione metabolism” and “Plant–pathogen interactions” in the roots (Figure 3a), “Photosynthesis—antenna proteins”, “Glyoxylate and dicarboxylate metabolism”, and “Cysteine and methionine metabolism” in the stem (Figure 3b), “Photosynthesis—antenna proteins” in the mature leaves (Figure 3c), and “Zeatin biosynthesis” and “Valine, leucine and isoleucine biosynthesis” in the new shoots (Figure 3d).

3.3. Analysis of DEGs Involved in Nitrogen Uptake and Transport Between the Two Cultivars in Different Tissues

AMT and NRT are responsible for ammonium and nitrate uptake and transport. Among the two cultivars, a total of three members of AMT and four members of NRT genes showed significant differences in any tissues (Figure 4a). Specifically, the expression of AMT1.2 and AMT3.1 was consistently up-regulated in TC12 compared to LJCY across all tissues. On the other hand, the expression of AMT3.3 and NRT2.5 was up-regulated in the leaf, stem, and shoot but down-regulated in the roots of TC12 compared to LJCY. Moreover, the expression levels of NRT3.2 and NRT2.5 were down-regulated more than three-fold in the shoots. And the expression levels of NRT2.5 and NRT2.7 in TC12 leaves were down-regulated compared with those of LJCY.
Amino acid is the predominant form of nitrogen transport from source leaves to new shoots, which requires the participation of amino acid transporters. We observed remarkable differences among 11 amino acid transporter genes which could be divided into two major categories (Figure 4b). Among them, the two AAP7 genes exhibited similar expression patterns as did the AAP3 genes, both were significantly more highly expressed in TC12 compared to LJCY across all four tissues analyzed. Additionally, AAP3 displayed higher expression levels in the leaves. The others, including AAP3, LHT1, CAT1, and 5 AAP6, were grouped into one cluster with generally lower expression levels observed in TC12 compared to LJCY. Except two, AAP6 and LHT1, which were more highly expressed in the TC12 shoots.

3.4. Analysis of DEGs Involved in Photosynthesis in Different Tissues Between the Two Cultivars

As most DEGs were significantly enriched in “Photosynthesis—antenna proteins”, we focused on analyzing the DEGs in this pathway. There were six LHC genes that showed significant differences in any tissues between the two cultivars (Figure 5a). All of these genes had a higher expression level in TC12 compared with LJCY (Figure 5b). Among them, the expression level of all LHCB1 genes were up-regulated more than three-fold in the roots of TC12 than those of LJCY. And the expression levels of LHCB3 were up-regulated 3.3-fold in the leaves of TC12 than those of LJCY.

3.5. Analysis of DEGs Involved in Cytokinin Metabolism in Different Tissues Between the Two Cultivars

The key genes involved in cytokinin metabolism, including isopentenyltransferase (IPT), Cytochrome P450 monooxygenase (CYP735A), Cytokinin dehydrogenase, and Zeatin O-glucosyltransferase (ZOG1), were further analyzed. Five members of IPT showed obvious differences between the two cultivars. Most of them showed a higher expression level in four tissues of TC12 compared with LJCY (Figure 6). Only IPT2 and IPT5 showed lower expression levels in the stems and leaves of TC12. The expression levels of three members of CYP735A were up-regulated in the new shoots of TC12. Five members of CKX, which degrades cytokinins, showed a significant change between the two cultivars. Among them, CKX3, CKX5, and CKX7 had lower expression levels in the new shoots of TC12. Four members of ZOG1, which catalyzes the O-glucosylation of cytokinin, showed lower expression levels in TC12.

3.6. Validation of RNA-Seq Results by qRT-PCR

In order to verify the reliability of the transcriptome data, eight DEGs were selected to perform qRT-PCR analysis. The results showed that the expression patterns of most genes were consistent with the RNA-seq data (Figure S2), demonstrating that our RNA-seq data are reliable.

4. Discussion

Previous studies have indicated genotype variations in nitrogen use efficiency among tea plants. For screening high-NUE genotypes, the soil and sand culture were considered as reliable methods [22]. However, these methods faced challenges in discerning whether nitrogen originated from the soil, fertilizer, or storage organs. To address this issue, a 15N tracing method has been widely employed in tea plants [23,24]. Our results revealed significant genotypic differences in N absorption and utilization among these genotypes, with TC12 exhibiting the highest NUE, followed by MZ. A previous investigation on 25 tea cultivars also reported genotype variations in NUE, with Zimudan displaying superior performance under N deficiency conditions [22]. Wang et al. (2005), using the 15N tracing method, demonstrated that MX and HD had higher NUEs [25]. Interestingly, these cultivars are known for their suitability as middle-leaf varieties used for Wulong tea production. Our findings from the 15N tracing method were consistent with the previous research, suggesting its effectiveness as an assessment tool for nitrogen uptake, assimilation, and distribution.
Plants are able to absorb and use inorganic nitrogen primarily depending on nitrate and ammonium transporters [4]. We observed significant differences in the expression of eight members of AMT and NRT genes between genotypes with different NUEs. Notably, the expression of AMT1.2 was up-regulated by three-fold in TC12, which is consistent with previous findings suggesting its high expression specificity in roots and inducibility by short-term nitrogen supply. ZC302, which had a higher ammonium uptake efficiency, exhibited a higher level of CsAMT1.2 expression compared to LJ43 and ZC108, with lower ammonium uptake efficiency [26]. These results suggest that high-NUE cultivars might enhance NH4+ absorption by increasing the expression level of CsAMT1.2. Additionally, we found the up-regulation of NRT2.4 and NRT3.2 in the roots of TC12. Zhang et al. (2021) reported that CsNRT2.4 showed strong expression specifically in tea roots and was greatly induced by N supply. Overexpressed CsNRT2.4 enhanced NO3 uptake rates at a low NO3 level in transgenic Arabidopsis thaliana [27]. Thus, we inferred that CsNRT2.4 might play an important role in the uptake of NO3 in tea roots. In addition, all these genes exhibited higher expression levels in the stems of TC12 than in those of LJCY. In plants, the partitioning of nitrogen from the source leaves to the sinks occurs in the phloem [28]. Previous studies have indicated that NRT2.5 and NRT2.4, which are expressed in the minor veins of Arabidopsis leaves, are involved in leaf remobilization and the phloem transport of nitrate [29]. These findings indicated that tea plants might increase the expression level of NRT2.4 and NRT2.5 to enhance nitrate remobilization from leaf to shoot.
Amino acids can not only be absorbed and utilized by roots, but also represent the main forms of N transported from source to sink. These movements are driven by plasmatic membrane-localized amino acid transporters (AATs) [30]. This study demonstrated distinct expression pattens of AATs, suggesting their functional specialization in different tissues. The expression level of AAP7 in TC12 was significantly higher than in LJCY, specifically in the roots and stem. A previous study found that CsAAP7.2 was highly expressed in the roots and stems of tea plants. It had the capacity to transport theanine and other amino acids [31]. Therefore, we speculated that TC 12 might regulate CsAAP7.2 to facilitate amino acid uptake from the soil and the long-distance transport of theanine. Additionally, TC12 exhibited a higher expression level of AAP7 in the leaves. Our previous study suggested that the expression level of AAP7 had a significantly negative correlation with Ndff in mature leaves during spring shoot development [32]. In addition, two members of AAP6 were found to be significantly more up-regulated in the shoots of TC12 than in those of LJCY. Our previous results showed that the expression of AAP6 in the shoots was significantly positively correlated with the amount of 15N in the shoots. Reducing the expression of CsAAP6 resulted in a significant decrease in the content of glutamic acid, aspartic acid, asparagine, glutamine, leucine, alanine, and arginine in the phloem exudates [13]. These findings indicate that TC12 might up-regulate AAP6 and AAP7 to improve nitrogen transport from source to sink. The lysine and histidine transporter (LHT) family represents a class of proteins involved in uptake, translocation, and utilization processes. In the present study, the expression of LHT1 was significantly higher in the shoots of TC12 compared to those of LJCY. In tea plants, 22 CsLHTs were identified from the ‘Shuchazao’ genome [33]. A previous study has demonstrated that LHT1 could uptake amino acids as a nitrogen source from the soil [34], suggesting potential tissue-specific roles for LHT1.
Nitrogen is the main component of RuBisCO and photosynthetic pigments, participating in the process of photosynthesis. The light-harvesting complex (LHC) proteins are members of a superfamily that can bind to chlorophyll and carotenoid to form pigment-protein complexes, which surround the photochemical reaction centers of PSI (LHCI) and PSII (LHCII). Our study showed that all LHC proteins were significantly higher in TC12 than LJCY. A previous study of tea plants showed that four of LHCII and two LHC proteins were down-regulated by more than twofold in nitrogen deficient leaves compared to the CK [15]. Furthermore, a plethora of photosynthesis-related proteins, including LHCA1, LHCA3, and LHCB6, were found to undergo acetylation upon ammonium supply in tea plants [14]. In addition, multiple research findings have consistently indicated that genotypes with a high nitrogen use efficiency often exhibited superior photosynthetic capacity compared to those with a low nitrogen use efficiency [35,36]. These results provide compelling evidence suggesting that a high-NUE genotype might enhance photosynthesis by up-regulating the expression of LHC.
Accumulating evidence has suggested that plant hormone regulation plays a crucial role in N metabolism, including absorption, transport, and assimilation [37]. In our study, the zeatin biosynthesis pathway was significantly enriched in new shoots. Zeatins, as a cytokinin, is known to play a crucial role in axillary bud differentiation and growth [38]. These findings indicate that zeatin might have an important function in nitrogen utilization. IPT and CYP735A are key genes involved in cytokinin synthesis. The present study showed that these two genes exhibited up-regulation in the shoots of TC12 compared to those of LJCY, suggesting that a high-NUE genotype might enhance cytokinin accumulation through the up-regulation of IPT and CYP735A.
We discovered that five members of ZOG1 were more down-regulated in TC12 than LJCY. Glycosylation is important for the regulation of the physiological activity of endogenous cytokinin, which can be glycosylated to form N-glycosides and O-glycosides. Cytokinin O-glucosides have been assumed to represent reversibly inactivated storage forms [39]. Thus, the discovery of cisZOGs plays an important role in maintaining cytokinin homeostasis. In shoots, the knockdown of OscZOG1 significantly improved panicle branching, tillering, grain number per panicle, and seed size, which are important agronomic traits for grain yield [40]. The present study showed that ZOG1 was down-regulated by more than eight-folds in TC12 than in LJCY, suggesting that ZOG1 might regulate zeatin homeostasis to influence bud sprouting. Furthermore, we found that the expression level of CKX in TC12 was down-regulated compared to that in LJCY. A previous study showed that the expression of CKX1 and CKX5 were up-regulated in an early sprouting tea variety [41]. These findings suggest that CKX also helps to maintain CKs homeostasis for bud sprouting, which is related to agronomic nitrogen use efficiency in tea plants.

5. Conclusions

In this study, we compared the nitrogen utilization efficiency of six tea cultivars by a 15N tracing method. The results show that TC12 had the highest NUE, while LJCY had lowest NUE. Then, the molecular mechanisms underlying this genotype difference were studied by RNA-seq. Most DEGs were significantly enriched by photosynthesis—antenna proteins and zeatin biosynthesis in mature leaves and new shoots, respectively. Genes involved in nitrogen uptake and transport, such as AMT1.2, NRT2.4, and AAP7, were up-regulated in TC12 compared with LJCY. TC12 had a higher expression level of LHC and a lower expression level of ZOG1. These genes could facilitate breeding for enhanced NUE in tea plants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14122824/s1, Figure S1: The number of DEGs in different tissues of TC12 compared with LJCY; Figure S2: Relative expression levels of DEGs in different tissues of TC12 compared with LJCY. Table S1: Primers used for real-time analysis; Table S2: Summary of the RNA-seq data; Table S3: Summary of unigenes annotation.

Author Contributions

Conceptualization, K.F. and M.W.; methodology, K.S.; software, K.S.; validation, S.G. and Z.L.; investigation, M.W.; resources, X.Q.; data curation, S.G.; writing—original draft preparation, M.W.; writing—review and editing, K.F.; visualization, M.W.; supervision, K.F.; funding acquisition, K.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agricultural Variety Improvement Project of Taian (2023NYLZ06), the Science and Technology Small and Medium-sized Enterprise Technology Innovation Project of Shandong Province (2022TSGC2232), and the Livelihood Project of Qingdao City (22-3-7-xdny-5-nsh).

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Min Wan was employed by the company China Tea (Beijing) Chain Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Ruan, J.; Gerendás, J.; Härdter, R.; Sattelmacher, B. Effect of root zone pH and form and concentration of nitrogen on accumulation of quality-related components in green tea. J. Sci. Food Agric. 2010, 87, 1505–1516. [Google Scholar] [CrossRef]
  2. Qiu, Z.H.; Liao, J.M.; Chen, J.H.; Li, A.S.; Lin, M.Y.; Liu, H.M.; Huang, W.; Sun, B.M.; Liu, J.; Liu, S.Q.; et al. Comprehensive analysis of fresh tea cv. Lingtou Dancong) leaf quality under different nitrogen fertilization regimes. Food Chem. 2024, 439, 138127. [Google Scholar] [CrossRef] [PubMed]
  3. Qiao, C.L.; Xu, B.; Han, Y.T.; Wang, J.; Wang, X.; Liu, L.L.; Liu, W.X.; Wan, S.Q.; Tan, H.; Liu, Y.Z.; et al. Synthetic nitrogen fertilizers alter the soil chemistry, production and quality of tea. A meta-analysis. Agron. Sustain. Dev. 2018, 38, 10. [Google Scholar] [CrossRef]
  4. Zhang, W.J.; Ni, K.; Long, L.Z.; Ruan, J.Y. Nitrogen transport and assimilation in tea plant (Camellia sinensis): A review. Front. Plant Sci. 2023, 14, 1249202. [Google Scholar] [CrossRef]
  5. Yang, X.D.; Ni, K.; Shi, Y.Z.; Yi, X.Y.; Zhang, Q.F.; Fang, L.; Ma, L.F.; Ruan, J.Y. Effects of long-term nitrogen application on soil acidification and solution chemistry of a tea plantation in China. Agric. Ecosyst. Environ. 2018, 252, 74–82. [Google Scholar] [CrossRef]
  6. Cheng, Y.; Wang, J.; Zhang, J.B.; Muller, C.; Wang, S.Q. Mechanistic insights into the effects of N fertilizer application on NO-emission pathways in acidic soil of a tea plantation. Plant Soil 2015, 389, 45–57. [Google Scholar] [CrossRef]
  7. Huang, X.C.; Lakshmanan, P.; Zhang, W.S.; Wang, X.Z.; Liu, B.; Ni, K.; Ruan, J.Y.; Shi, X.J.; Chen, X.P.; Zhang, F.S. Large loss of reactive nitrogen and the associated environmental damages from tea production in China. Agric. Ecosyst. Environ. 2025, 377, 109252. [Google Scholar] [CrossRef]
  8. Xie, X.Y.; Sun, Z.L.; Zhang, X.J.; Han, X.Y. Novel Aspects of Regulation of Nitrogen Responses in the Tea Plant (Camellia sinensis (L.)). Agronomy 2023, 13, 144. [Google Scholar] [CrossRef]
  9. Wang, Y.X.; Wei, K.; Ruan, L.; Bai, P.X.; Wu, L.Y.; Wang, L.Y.; Cheng, H. Systematic Investigation and Expression Profiles of the Nitrate Transporter 1/Peptide Transporter Family (NPF) in Tea Plant (Camellia sinensis (L.)). Int. J. Mol. Sci. 2022, 23, 6663. [Google Scholar] [CrossRef]
  10. Wang, Y.; Xuan, Y.M.; Wang, S.M.; Fan, D.M.; Wang, X.C.; Zheng, X.Q. Genome-wide identification, characterization, and expression analysis of the ammonium transporter gene family in tea plants (Camellia sinensis (L.)). Physiol. Plant. 2022, 174, e13646. [Google Scholar] [CrossRef]
  11. Tang, D.D.; Liu, M.Y.; Zhang, Q.F.; Fan, K.; Ruan, J.Y. Isolation and characterization of chloroplastic glutamine synthetase gene (CsGS2) in tea plant. Plant Physiol. Biochem. 2020, 155, 321–329. [Google Scholar] [CrossRef] [PubMed]
  12. Liu, Z.W.; Li, H.; Wang, W.L.; Wu, Z.J.; Cui, X.; Zhuang, J. CsGOGAT Is Important in Dynamic Changes of Theanine Content in Postharvest Tea Plant Leaves under Different Temperature and Shading Spreadings. J. Agric. Food Chem. 2017, 65, 9693–9702. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, J.; Sun, K.W.; Wang, Y.; Qian, W.J.; Sun, L.T.; Shen, J.Z.; Ding, Z.T.; Fan, K. Integrated metabolomic and transcriptomic analyses reveal the molecular mechanism of amino acid transport between source and sink during tea shoot development. Plant Cell Rep. 2024, 43, 28. [Google Scholar] [CrossRef] [PubMed]
  14. Jiang, J.T.; Gai, Z.S.; Wang, Y.; Fan, K.; Sun, L.T.; Wang, H.; Ding, Z.T. Comprehensive proteome analyses of lysine acetylation in tea leaves by sensing nitrogen nutrition. BMC Genom. 2018, 19, 840. [Google Scholar] [CrossRef]
  15. Lin, Z.H.; Chen, C.S.; Zhao, S.Q.; Liu, Y.; Zhong, Q.S.; Ruan, Q.C.; Chen, Z.H.; You, X.M.; Shan, R.Y.; Li, X.L.; et al. Molecular and physiological mechanisms of tea (Camellia sinensis (L.) O. Kuntze) leaf and root in response to nitrogen deficiency. BMC Genom. 2023, 24, 27. [Google Scholar] [CrossRef]
  16. Xu, W.L.; Li, J.; Zhang, L.Y.; Zhang, X.Y.; Zhao, H.; Guo, F.; Wang, Y.; Wang, P.; Chen, Y.Q.; Ni, D.J.; et al. Metabolome and RNA-seq Analysis of Responses to Nitrogen Deprivation and Resupply in Tea Plant (Camellia sinensis) Roots. Front. Plant Sci. 2022, 13, 932720. [Google Scholar] [CrossRef]
  17. Huang, H.; Yao, Q.Y.; Xia, E.H.; Gao, L.Z. Metabolomics and Transcriptomics Analyses Reveal Nitrogen Influences on the Accumulation of Flavonoids and Amino Acids in Young Shoots of Tea Plant (Camellia sinensis L.) Associated with Tea Flavor. J. Agric. Food Chem. 2018, 66, 9828–9838. [Google Scholar] [CrossRef]
  18. Liu, M.Y.; Burgos, A.; Zhang, Q.F.; Tang, D.D.; Shi, Y.Z.; Ma, L.F.; Yi, X.Y.; Ruan, J.Y. Analyses of transcriptome profiles and selected metabolites unravel the metabolic response to NH4+ and NO3 as signaling molecules in tea plant (Camellia sinensis L.). Sci. Hortic. 2017, 218, 293–303. [Google Scholar] [CrossRef]
  19. Yang, T.Y.; Li, H.P.; Tai, Y.L.; Dong, C.X.; Cheng, X.M.; Xia, E.H.; Chen, Z.P.; Li, F.; Wan, X.C.; Zhang, Z.L. Transcriptional regulation of amino acid metabolism in response to nitrogen deficiency and nitrogen forms in tea plant root (Camellia sinensis L.). Sci. Rep. 2020, 10, 6868. [Google Scholar] [CrossRef]
  20. Yang, Y.Y.; Wang, F.; Wan, Q.; Ruan, J.Y. Transcriptome analysis using RNA-Seq revealed the effects of nitrogen form on major secondary metabolite biosynthesis in tea (Camellia sinensis) plants. Acta Physiol. Plant. 2018, 40, 127. [Google Scholar] [CrossRef]
  21. Ruan, L.; Wei, K.; Li, J.W.; He, M.D.; Wu, L.Y.; Aktar, S.; Wang, L.Y.; Cheng, H. Responses of tea plants (Camellia sinensis) with different low nitrogen tolerances during recovery from nitrogen deficiency. J. Sci. Food Agric. 2022, 102, 1405–1414. [Google Scholar] [CrossRef] [PubMed]
  22. Chen, C.S.; Zhong, Q.S.; Lin, Z.H.; Yu, W.Q.; Wang, M.K.; Chen, Z.H.; You, X.M. Screening tea varieties for nitrogen efficiency. J. Plant Nutr. 2017, 40, 1797–1804. [Google Scholar] [CrossRef]
  23. Ma, L.F.; Shi, Y.Z.; Ruan, J.Y. Nitrogen absorption by field-grown tea plants (Camellia sinensis) in winter dormancy and utilization in spring shoots. Plant Soil 2019, 442, 127–140. [Google Scholar] [CrossRef]
  24. Tang, D.D.; Liu, M.Y.; Zhang, Q.F.; Ma, L.F.; Shi, Y.Z.; Ruan, J.Y. Preferential assimilation of NH4+ over NO3 in tea plant associated with genes involved in nitrogen transportation, utilization and catechins biosynthesis. Plant Sci. 2020, 291, 110369. [Google Scholar] [CrossRef]
  25. Wang, X.C.; Yang, Y.J.; Chen, L.; Wu, W. Using 15N labeling to study the differences in nitrogen fertilizer utilization efficiency among different varieties of tea trees. Fujian Tea 2005, 1, 4–5. [Google Scholar]
  26. Zhang, F.; Liu, Y.; Wang, L.Y.; Bai, P.X.; Ruan, L.; Zhang, C.C.; Wei, K.; Cheng, H. Molecular cloning and expression analysis of ammonium transporters in tea plants (Camellia sinensis (L.) O. Kuntze) under different nitrogen treatments. Gene 2018, 658, 136–145. [Google Scholar] [CrossRef]
  27. Zhang, F.; He, W.; Yuan, Q.Y.; Wei, K.; Ruan, L.; Wang, L.Y.; Cheng, H. Transcriptome analysis identifies CsNRT genes involved in nitrogen uptake in tea plants, with a major role of CsNRT2.4. Plant Physiol. Biochem. 2021, 167, 970–979. [Google Scholar] [CrossRef]
  28. Tegeder, M.; Masclaux-Daubresse, C. Source and sink mechanisms of nitrogen transport and use. New Phytol. 2018, 217, 35–53. [Google Scholar] [CrossRef]
  29. Lezhneva, L.; Kiba, T.; Feria-Bourrellier, A.B.; Lafouge, F.; Boutet-Mercey, S.; Zoufan, P.; Sakakibara, H.; Daniel-Vedele, F.; Krapp, A. The Arabidopsis nitrate transporter NRT2.5 plays a role in nitrate acquisition and remobilization in nitrogen-starved plants. Plant J. 2014, 80, 230–241. [Google Scholar] [CrossRef]
  30. Liu, L.; Yu, X.C.; Yan, Y.; He, C.X.; Wang, J.; Sun, M.T.; Li, Y.S. Amino Acid Transporters on Amino Acid Absorption, Transport and Distribution in Crops. Horticulturae 2024, 10, 999. [Google Scholar] [CrossRef]
  31. Li, F.; Lv, C.J.; Zou, Z.W.; Duan, Y.; Zhou, J.J.; Zhu, X.J.; Ma, Y.C.; Zhang, Z.L.; Fang, W.P. CsAAP7.2 is involved in the uptake of amino acids from soil and the long-distance transport of theanine in tea plants (Camellia sinensis L.). Tree Physiol. 2022, 42, 2369–2381. [Google Scholar] [CrossRef] [PubMed]
  32. Fan, K.; Zhang, Q.F.; Tang, D.D.; Shi, Y.Z.; Ma, L.; Liu, M.Y.; Ruan, J.Y. Dynamics of nitrogen translocation from mature leaves to new shoots and related gene expression during spring shoots development in tea plants (Camellia sinensis L.). J. Plant Nutr. Soil Sci. 2020, 183, 180–191. [Google Scholar] [CrossRef]
  33. Huang, W.; Ma, D.N.; Zaman, F.; Hao, X.L.; Xia, L.; Zhang, E.; Wang, P.; Wang, M.L.; Guo, F.; Wang, Y.; et al. Identification of the lysine and histidine transporter family in Camellia sinensis and the characterizations in nitrogen utilization. Hortic. Plant J. 2024, 10, 273–287. [Google Scholar] [CrossRef]
  34. Li, F.; Dong, C.X.; Yang, T.Y.; Bao, S.L.; Fang, W.P.; Lucas, W.J.; Zhang, Z.L. The tea plant CsLHT1 and CsLHT6 transporters take up amino acids, as a nitrogen source, from the soil of organic tea plantations. Hortic. Res. 2021, 8, 178. [Google Scholar] [CrossRef] [PubMed]
  35. Li, Q.; Ding, G.D.; Yang, N.M.; White, P.J.; Ye, X.S.; Cai, H.M.; Lu, J.W.; Shi, L.; Xu, F.S. Comparative genome and transcriptome analysis unravels key factors of nitrogen use efficiency in Brassica napus L. Plant Cell Environ. 2020, 43, 712–731. [Google Scholar] [CrossRef]
  36. Mahboob, W.; Sarwar, N.; Hafeez, O.B.; Arif, M.A.R.; Akhtar, M.; Yang, G.Z. Exploring genotypic variations in cotton associated with growth and nitrogen use efficiency. J. Plant Nutr. 2024, 47, 3231–3250. [Google Scholar] [CrossRef]
  37. Huang, Y.Z.; Ji, Z.; Zhang, S.Y.; Li, S. Function of hormone signaling in regulating nitrogen-use efficiency in plants. J. Plant Physiol. 2024, 294, 154191. [Google Scholar] [CrossRef]
  38. Ha, S.; Vankova, R.; Yamaguchi-Shinozaki, K.; Shinozaki, K.; Tran, L.S.P. Cytokinins: Metabolism and function in plant adaptation to environmental stresses. Trends Plant Sci. 2012, 17, 172–179. [Google Scholar] [CrossRef]
  39. Smehilová, M.; Dobrusková, J.; Novák, O.; Takác, T.; Galuszka, P. Cytokinin-Specific Glycosyltransferases Possess Different Roles in Cytokinin Homeostasis Maintenance. Front. Plant Sci. 2016, 7, 1264. [Google Scholar] [CrossRef]
  40. Shang, X.L.; Xie, R.R.; Tian, H.; Wang, Q.L.; Guo, F.Q. Putative zeatin O-glucosyltransferase OscZOG1 regulates root and shoot development and formation of agronomic traits in rice. J. Integr. Plant Biol. 2016, 58, 627–641. [Google Scholar] [CrossRef]
  41. Tang, J.W.; Chen, Y.; Huang, C.; Li, C.C.; Feng, Y.; Wang, H.Q.; Ding, C.Q.; Li, N.A.; Wang, L.; Zeng, J.M.; et al. Uncovering the complex regulatory network of spring bud sprouting in tea plants: Insights from metabolic, hormonal, and oxidative stress pathways. Front. Plant Sci. 2023, 14, 1263606. [Google Scholar] [CrossRef]
Figure 1. Comparison of nitrogen use efficiency among different tea cultivars. (a) Ndff in different tissues; (b) 15N accumulation in different tissues; (c) NUEs among different tea cultivars. The error bars indicate the standard deviations and the values corresponding to the mean ± standard deviation (SD) of three independent biological replicates. Different letters indicate significant difference (p < 0.05).
Figure 1. Comparison of nitrogen use efficiency among different tea cultivars. (a) Ndff in different tissues; (b) 15N accumulation in different tissues; (c) NUEs among different tea cultivars. The error bars indicate the standard deviations and the values corresponding to the mean ± standard deviation (SD) of three independent biological replicates. Different letters indicate significant difference (p < 0.05).
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Figure 2. Functional annotation classification for unigenes. (a) GO enrichment analysis for unigenes; (b) KOG enrichment analysis for unigenes; (c) KEGG enrichment analysis for unigenes. The red rectangle means the most enriched category.
Figure 2. Functional annotation classification for unigenes. (a) GO enrichment analysis for unigenes; (b) KOG enrichment analysis for unigenes; (c) KEGG enrichment analysis for unigenes. The red rectangle means the most enriched category.
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Figure 3. Enrichment of DEGs for the top 20 KEGG pathways in different tissues. (a) Roots; (b) stems; (c) leaves; and (d) new shoots. The red dotted rectangles means the most significantly encirched pathways.
Figure 3. Enrichment of DEGs for the top 20 KEGG pathways in different tissues. (a) Roots; (b) stems; (c) leaves; and (d) new shoots. The red dotted rectangles means the most significantly encirched pathways.
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Figure 4. Expression profiles of DEGs involved in nitrogen uptake and transport in different tissues between the two cultivars. (a) Ammonium and nitrate transporter; (b) amino acid transporter.
Figure 4. Expression profiles of DEGs involved in nitrogen uptake and transport in different tissues between the two cultivars. (a) Ammonium and nitrate transporter; (b) amino acid transporter.
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Figure 5. Expression profiles of DEGs involved in photosynthesis in different tissues between the two cultivars. (a) LHCB; (b) LHCB in tea leaves.
Figure 5. Expression profiles of DEGs involved in photosynthesis in different tissues between the two cultivars. (a) LHCB; (b) LHCB in tea leaves.
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Figure 6. Expression profiles of DEGs involved in cytokinin metabolism in different tissues between the two cultivars.
Figure 6. Expression profiles of DEGs involved in cytokinin metabolism in different tissues between the two cultivars.
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Wang, M.; Sun, K.; Qin, X.; Gong, S.; Li, Z.; Fan, K. Comparative Transcriptome Analysis Reveals a Tissue-Specific Pathway Involved in Nitrogen Utilization Between Genotypes with Different Nitrogen Use Efficiencies in Tea Plants (Camellia sinensis). Agronomy 2024, 14, 2824. https://doi.org/10.3390/agronomy14122824

AMA Style

Wang M, Sun K, Qin X, Gong S, Li Z, Fan K. Comparative Transcriptome Analysis Reveals a Tissue-Specific Pathway Involved in Nitrogen Utilization Between Genotypes with Different Nitrogen Use Efficiencies in Tea Plants (Camellia sinensis). Agronomy. 2024; 14(12):2824. https://doi.org/10.3390/agronomy14122824

Chicago/Turabian Style

Wang, Min, Kangwei Sun, Xujun Qin, Shuting Gong, Zhipeng Li, and Kai Fan. 2024. "Comparative Transcriptome Analysis Reveals a Tissue-Specific Pathway Involved in Nitrogen Utilization Between Genotypes with Different Nitrogen Use Efficiencies in Tea Plants (Camellia sinensis)" Agronomy 14, no. 12: 2824. https://doi.org/10.3390/agronomy14122824

APA Style

Wang, M., Sun, K., Qin, X., Gong, S., Li, Z., & Fan, K. (2024). Comparative Transcriptome Analysis Reveals a Tissue-Specific Pathway Involved in Nitrogen Utilization Between Genotypes with Different Nitrogen Use Efficiencies in Tea Plants (Camellia sinensis). Agronomy, 14(12), 2824. https://doi.org/10.3390/agronomy14122824

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