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Article

Time-Course Transcriptome and Phytohormonal Analysis of Blue-Light-Induced Adventitious Root Development of Tea Cuttings (Camellia sinensis (L.) Kuntze)

by
Yaozong Shen
1,2,†,
Hui Wang
3,†,
Xiao Han
2,
Kai Fan
2,
Jiazhi Shen
1,
He Li
2,
Shibo Ding
3,
Dapeng Song
3,
Yu Wang
2,* and
Zhaotang Ding
1,*
1
Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China
2
Tea Research Institute, Qingdao Agricultural University, Qingdao 266109, China
3
Tea Research Institute, Rizhao Academy of Agricultural Sciences, Rizhao 276800, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2023, 13(6), 1561; https://doi.org/10.3390/agronomy13061561
Submission received: 17 May 2023 / Revised: 2 June 2023 / Accepted: 5 June 2023 / Published: 6 June 2023
(This article belongs to the Special Issue Advances in Tea Agronomy: From Yield to Quality — Volume II)
Browse Figures
Versions Notes

Abstract

:
C. sinensis is an economically important crop for tea production that experiences increasing demand and good export potential. Therefore, crops need to be expanded, and high-quality planting material is required. Vegetative propagation by cuttings is the prevalent method; therefore, this paper explored its optimization potential modeled on cultivar ‘Jiukengzao’. This study wanted to deeply explore blue-light-induced adventitious root formation and development of tea cuttings, so we conducted short-term (0 h, 8 h, and 16 h) and long-term (30 d, 60 d, and 90 d) time-course analyses on tea cutting seedlings. Short-term, full-length transcriptome analysis showed that the expression of genes related to plant hormone signal transduction and auxin transport was highest at 16 h. Sixteen hours of light was considered as suitable for adventitious root growth and development of tea cuttings. Long-term phytohormone analysis showed that the trend of indole-3-carboxylic acid (ICA) change was: 60 d > 90 d > 30 d. Long-term, full-length transcriptome analysis showed that the gene expression trends in K2, K5, K6, and K8 clusters were: 90 d > 60 d > 30 d, and the opposite was observed in K1, K4, and K11 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that most of the genes in these seven clusters are involved in “plant hormone signal transduction (ko04075)”. This includes auxin early responsive protein AUX/IAA, auxin response factor ARF, auxin-responsive protein SAUR, etc. In addition, genes related to auxin transport and synthesis were identified as PIN1, 3, 4, PILS2, 6, 7, flavin-containing monooxygenase YUC9, and YUC10, and the expression trend of these genes was mostly consistent with the change trend of ICA content. This study further explained the molecular mechanism of blue-light-induced adventitious root formation and development of tea cuttings. It is recommended that blue light can be used to promote the adventitious root growth and development of tea cuttings in practical production.

1. Introduction

Short cutting is one of the most commonly used methods of tea reproduction, and the good formation of the adventitious root is a very important matter that involves the survival and cutting seedling formation rate and relates to the success of tea reproduction [1,2]. Adventitious root formation and development of tea cuttings are not only affected by the cuttings themselves (tea varieties, tenderness, etc.) but are also affected by many environmental factors such as light, temperature, humidity, and other external environmental conditions; light (type and duration) is especially important [3]. A previous study found that blue light (430 nm) could rapidly induce adventitious root formation from tea cuttings [4]. In order to further investigate the molecular mechanism of adventitious root formation and development induced by blue light, time-course sampling of cuttings treated with blue light (450 nm) was performed.
At present, blue light (400–480 nm) has been widely studied on tea plants. For example, under the treatment of blue shading nets, the synthesis of flavonoids and plant hormone signal transduction were regulated [5]. Different intensities of blue light could regulate lipid and flavonoid metabolism, and CsMYB might be a hub gene for blue light regulation of lipid and flavonoid metabolism [6]. Zheng et al. (2019) showed that blue light could promote the accumulation of catechins and anthocyanins in tea plants [7]. However, these studies are all about the quality of tea, and there is no report on the study of blue-light-induced adventitious root formation of tea cuttings.
Blue-light-induced root formation has been widely studied in other plants. In the study of Arabidopsis thaliana, blue light (470 nm) was able to induce adventitious root formation via the light signal sensor NPH3, and NPH3 regulates adventitious root formation by affecting PIN3-mediated auxin transport [8]. In the study of Rosmarinus officinalis, blue light (460 nm) not only significantly induced root formation at the base of cuttings but also induced root formation at the top and middle of cuttings [9]. Blue light also promoted the expression of genes related to IAA biosynthesis. In addition, in the study of Ocimum basilicum, blue light (460 nm) was able to rapidly induce root formation in cuttings [10]. These indicated that blue light could positively induce root formation and development, but no relevant time-course analysis has been performed yet.
Phytohormones play an important role in adventitious root formation and the growth of plant cuttings. Previous studies have shown that auxin could greatly promote the rooting efficiency of tea cuttings [1]. Fan et al. (2021) found that the content of indole-3-acetic acid (IAA), salicylic acid (SA), abscisic acid (ABA), and jasmonic acid (JA) in the rootable C. sinensis ‘Baihaozao’ shoots was significantly higher than that in the other two varieties [11]. In addition, light quality can affect phytohormone content. For example, blue light could promote the IAA content of Picea abies [12] as well as the ICA, ABA, and JA content of C. sinensis [4].
Plant hormone signal transduction plays an essential role in regulating the adventitious root formation and growth of plant cuttings. Wang et al. (2022) showed that SAUR, as a hub gene, might play an important role in the adventitious root formation of tea cuttings [2]. Meanwhile, blue light induced root formation of Ocimum basilicum cuttings by upregulating the auxin signal [10].
The purpose of this study is to further explore the molecular mechanism of blue-light-induced adventitious root formation and growth of tea cuttings and to look at the potential of optimizing the production of plant material of tea cuttings under blue light. In order to achieve this goal, three objectives were identified: (1) short-term response of tea cuttings to blue light, selecting appropriate lighting time; (2) assessing the dynamic changes in phytohormones in tea cuttings with blue light irradiation time (long-term); (3) identifying the dynamic changes in hormone-related gene expression with blue light irradiation time (long-term). This study revealed the regulatory mechanism of blue light in promoting adventitious root formation and growth of tea cuttings, and it provides new insights for tea breeding.

2. Materials and Methods

2.1. Plant Materials and Light Treatment

The branches of C. sinensis ‘Jiukengzao’ were obtained from the Rizhao Tea Science Research Institute, and they were then cut into cuttings (3–4 cm with a plump axillary bud). The cuttings were inserted into 32-hole plates and were exposed to blue light (450 nm, BL) with a light intensity of 100 μmol m−2 s−1 and an air humidity of 85 ± 5%. The experiment was established in the artificial climate chamber of the Rizhao Tea Science Research Institute.

2.1.1. Short-Term Treatment

The mature leaves of tea cuttings were sampled at 0 h (control), 8 h, and 16 h (temperature: 25 °C) and were stored in an ultra-low temperature refrigerator at −80 °C for full-length transcriptome sequencing. Each treatment had three replicates.

2.1.2. Long-Term Treatment

The tea short cuttings of C. sinensis ‘Jiukengzao’ (the cutting length was 3–4 cm, and it contained a healthy mature leaf and a plump axillary bud) were exposed to blue light (450 nm, BL) with a light intensity of 100 μmol m−2 s−1. Based on short-term treatment transcriptome data, we set the photoperiod to 16 h in the day (25 °C) and 8 h in the night (20 °C). The air humidity was 85 ± 5%. Water was sprayed every 3 d, and they were disinfected with potassium permanganate (in a ratio of 1 mL to 1 L of water) every 15 d. The mature leaves of tea cuttings were sampled at 30, 60, and 90 d of treatment, and they were stored in an ultra-low temperature refrigerator at −80 °C for phytohormone determination and full-length transcriptome sequencing. Each treatment had three replicates.

2.2. Phytohormone Determination

We randomly sampled five mature leaves from each sample, immediately placed them in liquid nitrogen, and then stored them at −80 °C in an ultra-low-temperature refrigerator for backup, with three replicates for each treatment. The 50 g of biological sample stored at ultra-low temperature was taken out and grinded into powder. Then, 10 μL of internal standard mixed solution with a concentration of 100 ng/mL was added. Then, the sample was extracted with 1 mL of methanol/water/formic acid (15:4:1, v/v/v). The mixture was vortexed for 10 min and then centrifugation for 5 min (12,000 r/min and 4 °C). The supernatant was then transferred to clean plastic microtubes, and it was then evaporated to dryness and dissolved in 100 μL of 80% methanol (V/V). It was then filtered through a 0.22 μm membrane filter. Finally, on the QTRAP6500 + LC-MS/MS platform, MetWare (http://www.metware.cn/, accessed on 12 December 2021) was used to measure phytohormone content. All measurements had three biological replicates.

2.3. RNA Extraction, ONT RNA-Seq, Quality Control, and Full-Length Transcript Identification

We randomly sampled five mature leaves from each sample, immediately placed them in liquid nitrogen, and then stored them at −80 °C in an ultra-low-temperature refrigerator for backup. Using the RNAprep Pure Plant Kit (Tianjin, Beijing, China), RNA was isolated from mature tea leaves under short-term and long-term treatment. Each treatment included three biological repeats. Then, 18 cDNA libraries were constructed using the PCR-cDNA Sequencing Kit (SQK-PCS109) and sequenced on the Nanopore PromethION platform. Full-length transcriptome analysis was conducted by Biomarker Technologies Co., Ltd. (Beijing, China). We filtered ONT RNA-seq raw reads (minimum average read quality score = 6, minimum read length = 350 bp) to remove low-quality reads. Results were mapped to the reference genome C. sinensis ‘cv.Shuchazao’ (CSS_ChrLev, http://tpia.teaplant.org/download.htm, accessed on 12 December 2021). After that, an FLNC transcript cluster was obtained and passed through the Pinfish package within each cluster (https://github.com/nanoporetech/pinfish, accessed on 12 December 2021). After removal, a consistent isomer was obtained. Finally, high-quality isomers were mapped to the reference genome of the tea plant. The cDNA_ Cupcake package was used to further collapse the map reading, with a minimum coverage rate of 85% and a minimum recognition rate of 90%. The 5′ difference was not considered when collapsing redundant transcripts. This produced a full non-redundant transcript.

2.4. Identification and Functional Annotation of Differentially Expressed Genes (DEGs)

Differentially expressed genes (DEGs) were identified from readings from 18 cDNA libraries successfully aligned with the reference genome. Readings with a matching quality higher than 5 were further used for quantification. Gene expression levels were estimated by counting per million. Using the DESeq R software package (1.18.0), differential expression analysis was performed on leaves under different light qualities. DESeq provides statistical routines for determining differential expression in digital gene expression data using a model based on a negative binomial distribution. The method of Benjamini and Hochberg [13] was used to adjust the generated p-value to control the error detection rate. Genes with a p-value < 0.05 and foldchange ≥ 2 were designated as differentially expressed genes.
Gene functional annotation was based on the following databases: NCBI Non-Redundant Protein Sequences (NR), Protein Families (Pfam), Homologous Clusters of Proteins (KOG/COG/eggNOG), Artificially Annotated and Reviewed Protein Sequence Database (Swiss Prot), Kyoto Encyclopedia of Genes and Genes (KEGG), and Gene Ontology (GO).

2.5. Quantitative Real-Time PCR Analysis

In order to verify the accuracy of transcriptome data, six different genes were randomly selected for expression level verification. Primer Premier 5.0 was used to design primers, and primer sequences are shown in Table S1. Quantitative real-time PCR (qRT-PCR) was performed using 2 × SYBR® Green premixed solution (DF, Beijing, China) on analytikjena-qTOWER2.2 fluorescence quantitative PCR instrument (Jena, Germany). Three biological replicates were analyzed. Glyceraldehyde 3-phosphate dehydrogenase (CsGAPDH) was used as a reference gene, and the relative expression was measured by 2−ΔΔCt.

2.6. Data Analysis

Statistical analysis was conducted using SPSS 20.0 software (SPSS Inc., Chicago, IL, USA). Single-factor analysis of variance (ANOVA) and Duncan multiple intervals were used to analyze the significant differences between physiological and hormone index data under different light quality treatments. When the p-value < 0.05, the differences were considered statistically significant. Graphs were created with Adobe Photoshop CC 2019 and GraphPad Prism.

3. Results

3.1. Selection of Light Time

In order to further determine the light time of long-term treatment, full-length transcriptome sequencing was conducted on mature leaves of cuttings treated with short-term treatment, including 0 h (control), 8 h, and 16 h. See the Appendix A for specific sequencing results.
Previous research showed that auxin transporter protein 1 AUX1, auxin early responsive protein AUX/IAA, auxin response factor ARF, DELLA, and protein phosphatase 2C PP2C are hub genes that play an important role in promoting adventitious root formation of tea cuttings under blue light. In addition, PIN1, 3, and 4 as well as PILS6 and 7 also had the highest expression levels under blue light, which might induce the expression of these genes and promote the transportation of ICA from mature leaves to the cutting base, thus promoting adventitious root formation [4]. Therefore, plant hormone signal-transduction-related genes were selected, including four AUX1s, four AUX/IAAs, five ARFs, six DELLAs, and two PP2Cs. Auxin-transport-related genes were also selected, including PIN and PIN-LIKES (PILS); PIN included PIN1, PIN3, and PIN4, and PILS included PILS2, PILS6, and PILS7. The expression analysis of these genes showed that except for one PIN1 and one PILS7, the other genes had the highest expression level at 16 h (Figure A2, Table S2). Based on the expression of these genes in this study and our previous research [4], 16 h of light was considered to be more suitable for cutting seedling growth and development, so 16 h of light was used for long-term treatment.

3.2. Phenotypic and Physiological Time-Course Analysis of Tea Cuttings under 16 h of Blue Light Treatment

The tea cutting seedlings were observed after treatment for 30, 60, and 90 d. The results showed that at 30 d, only callus was formed from the cutting seedlings; at 60 d, the cutting seedlings produced a few adventitious roots; and at 90 d, the adventitious roots of cutting seedlings were increased (Figure 1).

3.3. Phytohormone Time-Course Analysis of Tea Cuttings under 16 h of Blue Light

In order to reveal the internal mechanism of blue-light-induced rooting of tea cutting seedlings, phytohormone determination was conducted on mature leaves of cutting seedlings treated for 30, 60, and 90 d. The results showed that there were significant differences among multiple phytohormones. The descending order of the phytohormones concentrations due to time course were found in abscisic acid (ABA): 60 d > 30 d > 90 d; indole-3-carboxylic acid (ICA): 60 d > 90 d > 30 d; trans-Zeatin (tZ): 90 d > 60 d > 30 d; gibberellin A9 (GA9), and jasmonic acid (JA): 90 d > 30 d > 60 d. In addition, the trend of ICA/tZ and ICA/GA9: 60 d > 90 d > 30 d (Figure 2). These phytohormones played different roles in the process of adventitious root formation induced by blue light in tea cutting seedlings.

3.4. Time-Course Transcriptome of Tea Cuttings under 16 h of Blue Light

In order to study the effect of blue light treatment on gene expression of tea cuttings, nine RNA-seq libraries of C. sinensis ‘Jiukengzao’ were constructed at three light time points: 30 d, 60 d, and 90 d. An average of 5.42 million clean reads were generated per library, and an average of 4.84 million were mapped to the reference genome (Table A2).
Subsequently, using the filter criteria of p-value < 0.05 and foldchange ≥ 2, DEGs were identified by comparing 60 d to 30 d (30 d vs. 60 d) and 90 d to 30 d (30 d vs. 90 d). A total of 1236 DEGs were identified between 30 d and 60 d, of which 557 were significantly upregulated and 679 were significantly downregulated; a total of 2345 DEGs were identified at 30 d vs. 90 d, of which 1555 were significantly upregulated and 790 were significantly downregulated (Figure 3A).
A total of 2848 DEGs were identified using the two methods, of which 733 genes were common (Figure 3B). Analysis of KEGG pathway enrichment showed that these DEGs are mainly involved in “plant–pathogen interaction (ko04626)”, “circadian rhythm-plant (ko04712)”, and “photosynthesis-antenna protein (ko00196)” pathways (Figure 3C). COG analysis showed that these DEGs are mainly involved in “signal transduction mechanism” (Figure 3D).

3.5. K-Means Clustering Analysis

The K-means clustering algorithm was used to divide all identified DEGs into 11 clusters. The results showed that the expression trend of genes in K1, K4, and K11 clusters was consistent and gradually decreased, which was contrary to the growth trend of adventitious root of tea cuttings. The expression trend of genes in K2, K5, K6, K7, and K8 clusters was consistent and gradually increased, which was consistent with the growth trend of adventitious root of tea cuttings. The expression trend of genes in K3 and K9 clusters was consistent, increasing first and then decreasing. The expression trend of genes in K10 clustering was decreasing first and then increasing (Figure 4).
We analyzed the genes in these clusters using KEGG classification. The results indicated that many genes in K1, K4, and K11 clusters were involved in “plant hormone signal transduction (ko04075)”, “plant–pathogen interaction (ko04626)”, and “circadian rhythm-plant (ko04712)” pathways (Figure S1). Many genes in K2, K5, K6, K7, and K8 clusters were involved in “plant hormone signal transduction (ko04075)” and “plant–pathogen interaction (ko04626)” pathways (Figure S2). Many genes in K3 and K9 clusters were involved in “plant–pathogen interaction (ko04626)” and “circadian rhythm-plant (ko04712)” pathways (Figure S3). Many genes in K10 clusters were involved in “plant hormone signal transduction (ko04075)” and “plant–pathogen interaction (ko04626)” pathways (Figure S4). These pathways might play an important role in blue-light-induced adventitious root growth and development of tea cuttings.
We analyzed the genes in these clusters using COG. The results indicated that many genes in K1, K4, and K11 clusters were involved in “carbohydrate transport and metabolism”, “secondary metabolites biosynthesis, transport and catabolism” and “signal transduction mechanisms” (Figure S5). Many genes in K2, K5, K6, K7, and K8 clusters were involved in “signal transduction mechanisms” (Figure S6). Many genes in K3 and K9 clusters were involved in “carbohydrate transport and metabolism” and “signal transduction mechanisms” (Figure S7). Many genes in K10 clusters were involved in “carbohydrate transport and metabolism”, “secondary metabolites biosynthesis, transport and catabolism” and “signal transduction mechanisms” (Figure S8). These results indicated that signal transduction might play an important role in blue-light-induced adventitious root growth and development of tea cuttings.

3.6. Analysis of “Plant Hormone Signal Transduction” Pathway

Based on the previous study, plant hormone signal-transduction-related genes were selected and analyzed. Gene expression in K1, K4, and K11 clusters showed a contrary trend to adventitious root growth, while gene expression in K2, K5, K6, K7, and K8 clusters showed a similar trend to adventitious root growth. Therefore, “plant hormone signal transduction” pathway in K1, K4, and K11 as well as K2, K5, K6, K7, and K8 clusters were analyzed.
A total of 12 genes were selected in K1, K4, and K11 clusters, including one SAUR, one CRE1, three response regulator B-ARRs, one DELLA, one ethylene receptor ETR, two BRI1s, one MYC2, and one transcription factor TGA (Figure 5 and Table S3). The expression of the above genes decreased with the extension of treatment time, which was contrary to the growth trend of the root system.
A total of 49 genes were selected in K2, K5, K6, K7, and K8, including one AUX/IAA, one ARF, four SAURs, one gibberellin receptor GID1, five DELLAs, one abscisic acid receptor PYR/PYL, one serine/threonine protein kinase CTR1, two mitogen-activated protein kinase kinase SIMKKs, sixteen brassinosteroid-insensitive 1-associated receptor kinase 1 BAK1s, ten protein brassinosteroid-insensitive 1 BRI1s, four jasmonate ZIM domain-containing protein JAZs, two transcription factor MYC2s, and one regulatory protein NPR1 (Figure 6, Table S3). The expression of the above genes increased with the extension of treatment time, which was consistent with the growth trend of the root system.

3.7. Identification of Auxin Synthesis and Transportation-Related Genes

According to the results of the identification of auxin synthesis and transportation-related genes, two major categories of auxin-transport-related genes were identified, including PIN and PIN-LIKES (PILS). The PIN includes one PIN1, one PIN3, and one PIN4, and the PILS includes one PILS2, one PILS6, and two PILS7s. The expression analysis of these genes showed that the expression trend of most genes (except for one PILS2 and one PILS6) was: 60 d > 90 d > 30 d (Figure 7A, Table S4).
A total of three flavin monooxygenase genes YUC were identified, including one YUC9 and two YUC10s. Expression analysis of these genes showed that the expression trend of these three genes was: 60 d > 90 d > 30 d (Figure 7B, Table S5).

3.8. Validation of Genes by qRT-PCR

To verify the reliability of full-length transcriptome data, six genes were randomly selected for qRT-PCR validation. The results showed that the expression trend of most genes was consistent with the relative expression in qRT-PCR (Figure 8), proving that full-length transcriptome data is reliable and can be used for future research.

4. Discussion

4.1. Blue Light Induced Adventitious Root Development by Affecting Phytohormone Content

A lot of evidence shows that endogenous phytohormones play an important role in adventitious root formation and development [14,15,16]. ICA belongs to auxin. In this study, the content of ICA increased first and then decreased with time (Figure 2). This indicated that 30 to 60 d might be a high-incidence period for adventitious root formation. During this time, tea cuttings needed to transport more ICA from mature leaves to the base of cuttings, so the content increased. From 60 to 90 d, adventitious root formed and could produce ICA for their growth and development, the demand for ICA in mature leaves was reduced, so the content was decreased. ABA was considered an inhibitor of the rooting of Eucalyptus globulus [17], but in this study, its content also increased first and then decreased, increasing during the rooting period (Figure 2). In addition, in previous studies, ABA had the highest content under blue light, which was most favorable for rooting [4] and was highest in the new shoots of the easily rooted C. sinensis ‘Baihaozao’ [11]. These differences may be related to the different ABA distribution in different tissues, and the ABA metabolism in mature leaves is not the same as the base of cuttings. In addition, McAdam et al. (2016) reported that shoot-derived ABA could promote root growth [18]. In this study, the changing trend of GA9 and JA content was consistent, decreasing first and then increasing (Figure 2). As an inhibitor of adventitious root formation, GA content decreased during adventitious root formation but increased later, and this might be because the adventitious root has formed, and GA9 has no effect on the adventitious root growth. In addition, the ratio of ICA/GA9 showed a trend of first increasing and then decreasing (Figure 2). Fan et al.’s (2021) study showed that the IAA/GA3 ratio in the new shoots of the best rooting C. sinensis ‘Baihaozao’ was significantly higher than other varieties [11], which also explains the reason for the increase in ICA/GA9 ratio in the early stage (Figure 2). JA can rapidly accumulate in the rooting induction stage of Petunia hybrida cuttings [19], which indicates that JA can promote adventitious root formation. However, in this study, during adventitious root formation, JA content showed a downward trend and later increased. This might be due to the dominant role of ICA during adventitious root formation. tZ belongs to the cytokinin class, and its content gradually increased in this study. However, the ratio of ICA/tZ showed a trend of first increasing and then decreasing. It has been proven that the high ratio of auxin to cytokinin in vivo promotes root formation [1], which might be the reason for the increase in ICA/tZ ratio between 30 and 60 d.

4.2. Blue Light Induced Adventitious Root Development in Tea Cuttings by Affecting Auxin Synthesis

Auxin can promote cell division and differentiation, increase the number of cells, and promote adventitious roots formation and development in cuttings. ICA belongs to the auxin category. In this study, the content of ICA was: 60 d > 90 d > 30 d (Figure 2). The synthesis of auxin is achieved through tryptophan (Trp)-dependent and independent methods, with Trp-dependent methods being much better [20]. Among the Trp-dependent pathways, indole pyruvic acid (IPyA) is the most important, including the two-step reaction of converting Trp to IAA, in which YUC catalyzes an irreversible reaction: the oxidative decarboxylation of IPyA to form IAA [20,21,22,23]. In this study, the expression trend of YUC was: 60 d > 90 d > 30 d (Figure 7B). In our previous research, ICA had the highest content under blue light, which promoted the best rooting, and YUC was also expressed at the highest level under blue light [4]. The above results indicated that YUC might play an important role in blue-light-induced ICA synthesis.

4.3. Blue Light Induced Adventitious Root Development in Tea Cuttings by Affecting Expression of PIN and PILS Genes

At the early stage of cutting, there are no adventitious roots at the base of the cutting, and the auxin required for adventitious root formation is synthesized from mature leaves and transported to the base of the cutting. Currently, the main auxin transport carriers found are AUXIN1/LIKE-AUX1 (AUX/LAX), PIN-FORMED (PIN), and the ATP-binding cassette subfamily B (ABCB) family, in which AUX/LAX is an inner flow carrier and PIN and ABCB are outer flow carriers [24]. PIN-LIKES (PILS) has been identified as a new auxin-transport-promoting factor family. It has been demonstrated through cell sensitivity to auxin that PILS protein is necessary for auxin-dependent regulation of plant growth [25].
Auxin is a positive regulator during root development, and its transport is crucial [26,27]. Previous studies have shown that during the long-distance transportation of auxin from stem tip to root, PIN7 and ABCB1 are expressed at the stem tip and are responsible for the loading of auxin, while PIN1 and ABCB19 are responsible for maintaining the flow of auxin from stem to root [28,29,30]. This indicates that PIN1 can promote the transportation of auxin from the aboveground part to the underground part, thereby promoting the growth and development of the root system. At the same time, auxin efflux vector mutants such as PIN exhibit severe root defects [26]. AtPIN3 participates in the early formation of lateral roots, and AtPIN4 also plays an important role in root development [31,32,33,34]. In addition, studies have reported that the formation of adventitious roots in Arabidopsis induced by blue light is regulated by PIN3-mediated auxin transport. Blue light improved the content of auxin in adventitious roots, and the PIN3 protein level of blue light treatment was higher than that of dark conditions [8]. In this study, the expression trend of PIN1, PIN3, and PIN4 was 60 d > 90 d > 30 d (Figure 7A), which is consistent with the change trend of ICA content. In our previous study, PIN1, 3, and 4 had the highest expression levels under blue light [4]. This indicated that the expression of PIN gene was related to the content of auxin. In the research of tea plants, the CsPIN3 gene has been proven to be involved in regulating the growth and development of adventitious roots and the accumulation of auxin [35]. In summary, these results indicated that blue light promoted the transport of auxin from mature leaves to the base of cuttings by regulating the expression of PIN genes, thereby inducing adventitious root formation.
PILS proteins act as auxin carriers in the endoplasmic reticulum (ER), where they can stimulate intracellular auxin accumulation [25]. It has been proven that AtPILS1-7 are responsible for auxin polar transport and determine the direction in which auxin flows through the tissue [36]. Research has reported that PILS6 can control the nuclear abundance of auxin to regulate auxin response and subsequent root growth [37]. In the study of tea plants, CsPILS played an important role in the adventitious root formation process of cutting seedlings [35]. In this study, the expression trend of PILS6 and PILS7 was 60 d > 90 d > 30 d (Figure 7A), which is consistent with the change trend of ICA content. In our previous study, PILS6 and 7 had the highest expression levels under blue light [4]. The above results indicated that blue light promoted the transport of auxin from mature leaves to the base of cuttings by regulating the expression of PILS genes, thereby inducing adventitious root formation and development.

4.4. Blue Light Induced Adventitious Root Development in Tea Cuttings by Affecting Expression of Hormone Signal-Transduction-Related Genes

The development of adventitious and lateral roots in plants is regulated by auxin [32]. Plants can quickly respond to changes in auxin levels through auxin-response factors such as AUX/IAA, ARF, SAUR, and GH3 [38,39,40]. IAA17 is involved in some typical phenotypes of auxin signal transduction control, such as adventitious root and root hair formation [41,42]. Study has shown that signal transduction mediated by AUX/IAA and ARF played an important role in regulating plant root formation [41]. ZmIAA10 can interact with ZmARF25 and 34, thereby affecting the root growth and development of Zea mays [43]. In this study, the expression trend of AUX/IAA and ARF is consistent (Figure 6), indicating that there may be a synergistic effect between AUX/IAA and ARF in blue-light-induced adventitious root formation. In addition, in the study of Arachis hypogaea, the expression level of AhARF14/26/45 was positively correlated with root length, root surface area, and root tip number [44]. In the study of Arabidopsis, overexpression of SAUR76 promoted taproot elongation [45]. In this study, the expression levels of AUX/IAA, ARF, and SAUR increased with the prolongation of treatment time, which had the same trend as the root formation and growth (Figure 6). In addition, AUX/IAA, ARF, and SAUR act as hub genes, which had the highest expression levels under blue light in our previous study [4]. The above results indicated that AUX/IAA, ARF, and SAUR played important roles in adventitious root formation and development of tea cuttings induced by blue light.
In addition to auxin signal pathway involved in blue-light-induced adventitious roots, other hormone signal pathways were also involved, including gibberellin, abscisic acid, ethylene, brassinolide, jasmonic acid, and salicylic acid signal pathways. Fan et al. (2021) conducted a study on three cutting seedlings with different rooting abilities, indicating that the expression of DELLA in the shoots of C. sinensis ‘Baihaozao’ with the best rooting ability was higher than that of the other two varieties [11]. In addition, DELLA expression increased during the adventitious root formation of Populus, which is similar to the results of the current research [46]. PYR/PYL can promote PP2A activity, thus eliminating PINIOD (PID)-mediated phosphorylation of PIN protein, which facilitates auxin efflux transport and maintains normal root development [47]. In this study, the expression of PYR/PYL increased with treatment time (Figure 6), indicated that it might play an important role in the process of blue-light-induced adventitious root growth and development. CRE1, as a cytokinin receptor, and B-ARR, as a positive regulatory factor for cytokinin, gradually decreased in expression (Figure 5). Previous research has shown that MtCRE1 negatively regulates root growth and development [48]. At the same time, in the study of Populus, PtRR13 (B-ARR) was considered a negative regulator of the adventitious root [49]. In summary, blue light might promote adventitious root formation and growth of tea cuttings by regulating the expression of DELLA, PYR/PYL, B-ARR, and CRE1.
Brassinolide (BR) signal transduction can influence many plant processes, including nutrition and reproductive growth, root growth, and photomorphogenesis. BAK1 is the receptor gene of BR, and the up-regulated expression of BAK1 can promote the root growth of Brassica napus [50], which proves that BAK1 may have a positive response to root growth. At the same time, in the study of Cucumis sativus, the upregulated expression of BAK1 had a positive regulatory effect on the adventitious root [51]. In the study of Arabidopsis, the expression of BRI1 rescued the root growth of the bri1 mutant [52,53], indicating that BRI1 was positively regulating root growth. In this study, the expression levels of multiple BAK1 and BRI1 increased with adventitious root growth, which indicated that blue light might promote tea cutting adventitious root growth and development by inducing the expression of BAK1 and BRI1. JA regulates plant growth and development, including root growth, flowering, etc. As the response factors of JA, JAZ and MYC2 expression levels increased and decreased, respectively, with the prolongation of blue light irradiation time. In the study of Arabidopsis, JAZ4 has been shown to promote root elongation [54]. In the study of Populus, transgenic methods were used to prove that PttMYC2 has negative effects on the adventitious root [55], and AtMYC2 has also been proved to be a negative regulator of adventitious root development in Arabidopsis [56,57]. In conclusion, blue light might induce the adventitious root formation and development by regulating the expression of BAK1, BRI1, JAZ, and MYC2.

5. Conclusions

This study further explored the molecular mechanism of adventitious root formation and development in tea cuttings induced by blue light through time-course analysis. We draw the following conclusion: (1) based on the full-length transcriptome results (short-term), we further determined that 16 h of light was suitable for adventitious root formation and growth of tea cuttings. (2) Long-term processed phytohormone data indicated that the change in ICA content was: 60 d > 90 d > 30 d. (3) According to our long-term full-length transcriptome analysis, blue light induced adventitious root formation and growth of tea cuttings by promoting the expression of hormone signal transduction, auxin synthesis, and transportation-related genes such as AUX/IAA, ARF, SAUR, YUC, PIN, and PILS. In conclusion, our research laid a foundation for future research on the impact of blue light on tea growth and development, and it also provided a theoretical basis for supplementing blue light to induce adventitious root formation in actual production.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13061561/s1, Table S1: Primers used for real-time PCR analysis; Table S2: Annotation of short-term plant hormone signal-transduction-related genes; Table S3: Annotation of long-term plant hormone signal-transduction-related genes; Table S4: Annotation of auxin-transport-related genes; Table S5: Annotation of auxin-synthesis-related genes. Figure S1: KEGG classification diagram of genes in K1, K4, and K11 clusters; Figure S2: KEGG classification diagram of genes in K2, K6, K7, and K8 clusters; Figure S3: KEGG classification diagram of genes in K3 and K9 clusters; Figure S4: KEGG classification diagram of genes in K10 cluster; Figure S5: COG classification diagram of genes in K1, K4, and K11 clusters; Figure S6: COG classification diagram of genes in K2, K6, K7, and K8 clusters; Figure S7: COG classification diagram of genes in K3 and K9 clusters; Figure S8: COG classification diagram of genes in K10 cluster.

Author Contributions

Y.S. conducted experiments, analyzed the data, and wrote the manuscript. H.W. conducted experiments, collected samples, and statistically analyzed data. X.H. participated in manuscript writing and picture production. Z.D., K.F. and Y.W. participated in the experimental design and revised the manuscript. J.S. revised the manuscript. H.L., S.D. and D.S. collected samples. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Technology System of Modern Agricultural Industry in Shandong Province (SDAIT19-01) and the Special Foundation for Distinguished Taishan Scholar of Shandong Province (ts201712057), the Project of Agricultural Science and Technology Fund in Shandong Province (2019LY002, 2019YQ010, and 2019TSLH0802), Shandong Agricultural Seed Improvement Project (2020LZGC010), the Project of Rizhao Natural Science Foundation Youth Fund (RZ2021ZR48), the Livelihood Project of Qingdao City (22-3-7-xdny-5-nsh), and the Agricultural Science and Technology Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2023F18, CXGC2023A11).

Data Availability Statement

The raw data for RNA-seq have been uploaded to the NCBI SRA with accession number PRJNA948654.

Acknowledgments

We thank the whole research group for their active role in the experimental process, data analysis, and manuscript revision.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

A total of 9 ONT RNA-seq libraries of C.Jiukengzhao were constructed, each library generating an average of 6.34 million clean reads, with more than 90% being mapped to the reference genome (Table A1).
DEGs were identified using the methods of 8 h vs. 0 h (0 h vs. 8 h) and 16 h vs. 0 h (0 h vs. 16 h). A total of 1448 DEGs were identified during 0 h vs. 8 h, of which 817 were significantly up-regulated and 631 were significantly down-regulated (Figure A1A). KEGG enrichment analysis showed that many DEGs were significantly enriched in “photosynthesis-antenna proteins (ko00196)”, “circadian rhythm-plant (ko04712)”, and “glutathione metabolism (ko00480)” (Figure A1B). A total of 4299 DEGs were identified between 0 h and 16 h, of which 2195 were significantly up-regulated and 2104 were significantly down-regulated (Figure A1C). KEGG enrichment analysis showed that many DEGs were significantly enriched in “ribosome (ko03010)”, “circadian rhythm-plant (ko04712)”, and “glutathione metabolism (ko00480)” (Figure A1D).
890 DEGs identified in 0 h vs. 8 h and 0 h vs 16 h were common (Figure A1E). KEGG enrichment analysis was performed on these genes. The results showed that many DEGs were significantly enriched in “ribosome (ko03010)”, “circadian rhythms-plant (ko04712)”, “photosynthesis-antenna proteins (ko00196)”, and “glutathione metabolism (ko00480)” (Figure A1F).
Table
Table A1. Overview of transcriptome data.
Table A1. Overview of transcriptome data.
Sample IDNumber of Clean Reads (Except rRNA)Number of Full-Length ReadsFull-Length Percentage (FL%)
0h_16,383,3275,832,34491.37%
0h_26,281,3735,665,38690.19%
0h_36,501,1015,891,75790.63%
8h_15,960,3175,405,46690.69%
8h_26,158,4865,553,75190.18%
8h_36,799,0506,224,17291.54%
16h_16,847,3266,213,13790.74%
16h_25,748,2515,174,76790.02%
16h_36,400,3295,788,70390.44%
total57,079,560
average6,342,173
Agronomy 13 01561 g0a1 550
Figure A1. (A) Number of DEGs identified in 0 h vs. 8 h, The Y-axis represents the number of DEGs; (B) KEGG enrichment map of DEGs in 0 h vs. 8 h (Top 20 statistics of KEGG pathway); (C) number of DEGs identified at 0 h vs. 16 h, The Y-axis represents the number of DEGs; (D) KEGG enrichment map of DEGs in 0 h vs. 16 h (Top 20 statistics of KEGG pathway); (E) The VENN diagram of pairwise comparison; (F) KEGG enrichment map of all DEGs (Top 20 statistics of KEGG pathway).
Figure A1. (A) Number of DEGs identified in 0 h vs. 8 h, The Y-axis represents the number of DEGs; (B) KEGG enrichment map of DEGs in 0 h vs. 8 h (Top 20 statistics of KEGG pathway); (C) number of DEGs identified at 0 h vs. 16 h, The Y-axis represents the number of DEGs; (D) KEGG enrichment map of DEGs in 0 h vs. 16 h (Top 20 statistics of KEGG pathway); (E) The VENN diagram of pairwise comparison; (F) KEGG enrichment map of all DEGs (Top 20 statistics of KEGG pathway).
Agronomy 13 01561 g0a1
Agronomy 13 01561 g0a2 550
Figure A2. Expression map of genes associated with plant hormone signal transduction and auxin transport (CPM). The Y-axis represents the expression level of genes (CPM). Lowercase letters represent significant differences.
Figure A2. Expression map of genes associated with plant hormone signal transduction and auxin transport (CPM). The Y-axis represents the expression level of genes (CPM). Lowercase letters represent significant differences.
Agronomy 13 01561 g0a2
Table
Table A2. Overview of transcriptome sequencing.
Table A2. Overview of transcriptome sequencing.
Sample IDNumber of Clean Reads (Except rRNA)Number of Full-Length ReadsFull-Length Percentage (FL%)
30d_15,025,9144,509,14289.72%
30d_25,299,1754,748,26489.60%
30d_35,411,1124,857,78989.77%
60d_16,560,5995,877,34789.59%
60d_25,677,0685,132,50490.41%
60d_34,438,6313,999,76390.11%
90d_15,334,6814,340,17981.36%
90d_25,052,9514,595,47190.95%
90d_36,035,3575,527,89691.59%
total48,835,488
average5,426,165

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Figure 1. Phenotypes of tea cutting seedlings: 30 d, 60 d, and 90 d.
Figure 1. Phenotypes of tea cutting seedlings: 30 d, 60 d, and 90 d.
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Figure 2. Time-course changes in phytohormone content in mature leaves of tea cutting seedlings: 30 d, 60 d, and 90 d. The Y-axis represents the phytohormone content (unit: ng/g). A total of 50 mg of fresh tea leaves per sample were measured. The significance level was (n = 3, p < 0.05) based on a Duncan multiple intervals significant difference test. Lowercase letters represent significant differences.
Figure 2. Time-course changes in phytohormone content in mature leaves of tea cutting seedlings: 30 d, 60 d, and 90 d. The Y-axis represents the phytohormone content (unit: ng/g). A total of 50 mg of fresh tea leaves per sample were measured. The significance level was (n = 3, p < 0.05) based on a Duncan multiple intervals significant difference test. Lowercase letters represent significant differences.
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Figure 3. (A) Comparison of the number of different genes in mature leaves of tea cutting seedlings under different treatments. The Y-axis represents the number of DEGs. (B) Venn diagram shows the number of DEGs per comparison between different processes; (C) KEGG enrichment map of different genes between different treatments (top 20 statistics of KEGG pathway); (D) COG enrichment map of different genes between different treatments.
Figure 3. (A) Comparison of the number of different genes in mature leaves of tea cutting seedlings under different treatments. The Y-axis represents the number of DEGs. (B) Venn diagram shows the number of DEGs per comparison between different processes; (C) KEGG enrichment map of different genes between different treatments (top 20 statistics of KEGG pathway); (D) COG enrichment map of different genes between different treatments.
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Figure 4. 11 K-means clustering were determined according to the DEG expression level from 30 d to 90 d. Color lines represent the expression trend of genes.
Figure 4. 11 K-means clustering were determined according to the DEG expression level from 30 d to 90 d. Color lines represent the expression trend of genes.
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Figure 5. Plant hormone signaling pathways. The genes marked in the black box in the figure were selected from K1, K4, and K11 clusters.
Figure 5. Plant hormone signaling pathways. The genes marked in the black box in the figure were selected from K1, K4, and K11 clusters.
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Figure 6. Plant hormone signaling pathways. The genes marked in the black box in the figure were selected from K2, K5, K6, K7 and K8 clusters.
Figure 6. Plant hormone signaling pathways. The genes marked in the black box in the figure were selected from K2, K5, K6, K7 and K8 clusters.
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Figure 7. (A) G Expression level of genes related to auxin transport. (B) Expression level of genes related to auxin synthesis. The Y-axis represents the expression level of genes represented by counts per million (CPM). The significance level was (n = 3, p < 0.05) based on a Duncan multiple intervals significant difference test. Lowercase letters represent significant differences.
Figure 7. (A) G Expression level of genes related to auxin transport. (B) Expression level of genes related to auxin synthesis. The Y-axis represents the expression level of genes represented by counts per million (CPM). The significance level was (n = 3, p < 0.05) based on a Duncan multiple intervals significant difference test. Lowercase letters represent significant differences.
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Figure 8. qRT-PCR validation results using CsGAPDH as an internal control. The left Y-axis represents counts per million (CPM) values for Oxford Nanopore Technologies (ONT) RNA-Seq, and the right Y-axis represents relative qRT-PCR expression levels (the relative expression was measured by 2−ΔΔCt). In the figure, the bars represent ONT RNA-Seq, and the lines represent qRT-PCR.
Figure 8. qRT-PCR validation results using CsGAPDH as an internal control. The left Y-axis represents counts per million (CPM) values for Oxford Nanopore Technologies (ONT) RNA-Seq, and the right Y-axis represents relative qRT-PCR expression levels (the relative expression was measured by 2−ΔΔCt). In the figure, the bars represent ONT RNA-Seq, and the lines represent qRT-PCR.
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MDPI and ACS Style

Shen, Y.; Wang, H.; Han, X.; Fan, K.; Shen, J.; Li, H.; Ding, S.; Song, D.; Wang, Y.; Ding, Z. Time-Course Transcriptome and Phytohormonal Analysis of Blue-Light-Induced Adventitious Root Development of Tea Cuttings (Camellia sinensis (L.) Kuntze). Agronomy 2023, 13, 1561. https://doi.org/10.3390/agronomy13061561

AMA Style

Shen Y, Wang H, Han X, Fan K, Shen J, Li H, Ding S, Song D, Wang Y, Ding Z. Time-Course Transcriptome and Phytohormonal Analysis of Blue-Light-Induced Adventitious Root Development of Tea Cuttings (Camellia sinensis (L.) Kuntze). Agronomy. 2023; 13(6):1561. https://doi.org/10.3390/agronomy13061561

Chicago/Turabian Style

Shen, Yaozong, Hui Wang, Xiao Han, Kai Fan, Jiazhi Shen, He Li, Shibo Ding, Dapeng Song, Yu Wang, and Zhaotang Ding. 2023. "Time-Course Transcriptome and Phytohormonal Analysis of Blue-Light-Induced Adventitious Root Development of Tea Cuttings (Camellia sinensis (L.) Kuntze)" Agronomy 13, no. 6: 1561. https://doi.org/10.3390/agronomy13061561

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