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Article

Transcriptome and Metabolome Analysis Revealed the Freezing Resistance Mechanism in 60-Year-Old Overwintering Camellia sinensis

by
Hui Wu
,
Zixian Wu
,
Yuanheng Wang
,
Jie Ding
,
Yalin Zheng
,
Heng Tang
and
Long Yang
*
Agricultural Big-Data Research Center and College of Plant Protection, Shandong Agricultural University, Taian 271018, China
*
Author to whom correspondence should be addressed.
Biology 2021, 10(10), 996; https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100996
Submission received: 3 September 2021 / Revised: 28 September 2021 / Accepted: 1 October 2021 / Published: 3 October 2021
(This article belongs to the Special Issue Recent Advances in Plant Metabolomics: From Metabolic Pathways to Health Impact)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

The freezing stress during overwintering brings great challenges to the normal growth of Camellia sinensis. The current research on C. sinensis mainly focuses on cold resistance, but less on freezing resistance. In the present study, the transcriptome and metabolome of C. sinensis under freezing stress were studied. Results showed that Pyr/PYL-PP2C-SnRK2 played a critical role in the signal transduction of freezing stress. Three metabolic pathways including phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis contributed to the freezing resistance of C. sinensis. This study provides substantial insights for the breeding of C. sinensis.

Abstract

Freezing stress in winter is the biggest obstacle to the survival of C. sinensis in mid-latitude and high-latitude areas, which has a great impact on the yield, quality, and even life of C. sinensis every year. In this study, transcriptome and metabolome were used to clarify the freezing resistance mechanism of 60-year-old natural overwintering C. sinensis under freezing stress. Next, 3880 DEGs and 353 DAMs were obtained. The enrichment analysis showed that pathways of MAPK and ABA played a key role in the signal transduction of freezing stress, and Pyr/PYL-PP2C-SnRK2 in the ABA pathway promoted stomatal closure. Then, the water holding capacity and the freezing resistance of C. sinensis were improved. The pathway analysis showed that DEGs and DAMs were significantly enriched and up-regulated in the three-related pathways of phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis. In addition, the carbohydrate and fatty acid synthesis pathways also had a significant enrichment, and the synthesis of these substances facilitated the freezing resistance. These results are of great significance to elucidate the freezing resistance mechanism and the freezing resistance breeding of C. sinensis.

1. Introduction

Camellia sinensis is a perennial evergreen flowering plant of the genus Camellia in the Camellia family, which has the characteristics of loving a warm and humid climate [1,2,3]. In addition, it can grow regularly at 10–35 °C but becomes nearly dead at −10 °C. Shandong province is a northernmost tea growing region in China and has the highest latitude in tea growing regions of the world except for the Mediterranean climate regions [4]. In the 1960s, Chinese “South Tea being introduced to the North” was one of the most successful examples of introduction in the world, and Tai’an, Shandong, was one of the successful sites for the first trial planting.
Freezing stress in winter is the biggest obstacle to the survival of C. sinensis in mid-latitude and high-latitude areas and an important factor causing crop yield reduction and even death [5,6,7,8]. When plants are subjected to freezing stress, photosynthesis will be inhibited and cell structure destroyed, resulting in oxidative damage, metabolic disorder, cell damage, and other phenomena [9,10,11]. Simultaneously, plants will express a large number of resistance genes and metabolites when subjected to freezing stress [12].
In order to clarify the mechanism of freezing resistance in plants, scientists carried out a lot of research in molecular biology and bioinformatics [13,14]. Under freezing stress, Fritillaria cirrhosa would accumulate high H2O2 and activate antioxidant systems such as catalase, anthocyanins, phenols, SOD, and ascorbic acid peroxidase [15]. After the relief of freezing stress, the photosynthesis of F. cirrhosa would return to the normal level. Studies on Triticum aestivum leaves under freezing stress showed that T. aestivum could improve frost resistance by the accumulation of sucrose and the coordination of salicylic acid and carbon [16]. Transcriptional sequencing of Brassica napus under freezing stress revealed 3905 DEGs, of which the up-regulated DEGs were mainly in hormone signal transductions, energy metabolism, and resistance related gene families [17]. With the development of bioinformatics, more and more research has been done to explore the mechanism of plant responses to abiotic stresses by combining transcriptome with metabolome [18,19]. Transcriptome and metabolome of Lycium barbarum under salt stress were analyzed to obtain 1396 DEGs and 71 DAMs [20]. The pathway analysis showed that the metabolism of flavonoids could improve the salt tolerance of L. barbarum. The flavonoid and phenolic compounds produced by phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways had a positive response on abiotic stresses [21]. Therefore, it is of great significance to probe these metabolic pathways to understand the mechanism of plant resistance. The research on transcriptome and metabolome of C. sinensis mainly focuses on the cold treated greenhouse materials. The results showed that there was a close relationship between cold stress and drought stress, and ICE (inducer of CBF expression) and HSP (heat shock proteins) gene families had a positive response under cold stress in C. sinensis [22,23].
C. sinensis is one of the most important cash crops in the world [24,25]. Although it has the characteristics of a perennial, a large number of C. sinensis trees are frozen to death during overwintering every year and need to be replanted in the second year, which consumes a lot of labor and financial resources. In this study, transcriptome and metabolome were used to examine the freezing resistance mechanism of 60-year-old C. sinensis trees under natural freezing stress, and to clarify the gene regulation of C. sinensis trees under the extreme temperature of −14 °C. Furthermore, metabolic pathways and the metabolic state of C. sinensis trees under freezing stress were analyzed. From the gene and metabolite level, the reason why these C. sinensis trees can spend 60 winters in Shandong province was explored, which laid a foundation for the excavation of the freezing resistance genes and the cultivation of the freezing resistance varieties of C. sinensis.

2. Materials and Methods

2.1. Plant Material and Sampling

The experiment was carried out in the 60-year-old overwintering C. sinensis garden (36°13′9″ N, 117°3′16″ E) on Mount Tai (Tai ’an, Shandong province, China). The first sampling (freezing stress) took place on 30 December 2020, with a minimum temperature of −14 °C and an average temperature of −10 °C as the lowest temperature of the year. On 30 March 2021, a second sample (control) was taken of the tea plants at an average temperature of 15 °C. Tea trees with a good growth status were selected for the first sampling, the tea trees were marked after sampling, and the same tea tree was selected for the second sampling. These samples were immediately frozen in liquid nitrogen and stored at −80 °C for subsequent sequencing [26]. The experiment was divided into the freezing stress group (FS) and control check group (CK). A total of 9 samples were sampled from each group, of which 3 samples (FS_1, FS_2, FS_3, CK_1, CK_2, CK_3) were sequenced for transcriptome and 6 samples (FS-1, FS-2, FS-3, FS-4, FS-5, FS-6, CK-1, CK-2, CK-3, CK-4, CK-5, CK-6) were analyzed for metabolome.

2.2. Transcriptome Sequencing, Identification, and Analysis of Differentially Expressed Genes (DEGs)

Using the Truseq RNA (RNAprep Pure Plant Kit, Poly-saccharides&Polyphenolics-rich, centrifugal column, Beijing, China) sample preparation kit, the RNA sample was prepared according to the manufacturer’s protocol [27]. RNA quality was evaluated by agarose gel electrophoresis and OD260/230 ratio, and the cDNA library was constructed after the samples were qualified [28]. The library was qualified by an Agilent 2100 bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and quantified by Qubit and qPCR [29]. Then, the constructed cDNA library was sequenced with Illumina HiSeq 2500 and transformed into original Sequenced Reads using BCL2FASTQ [30,31]. The raw data were evaluated with FASTQC, followed by filtering (removing the containing adapter and low-quality reads) raw data with FASTP, and then again using FASTQC to perform quality control [32,33]. Hisat2 was used to map the clean data to “shuchazao” reference genome on 10 May 2021 (http://tpia.teaplant.org/download.html, accessed on 10 May 2021) and calculate mapping information [34]. The Stringtie software was used to assemble transcripts and predict expression levels using tea transcriptome data [35]. R language was used to analyze the correlation between tea transcripts and then draw the heat map [36].
The differentially expressed genes (DEGs) of log2fc ≥ 2 and p-value ≤ 0.05 under freezing stress were screened by lemma, and the R package was used to make volcano plots. DEGs were annotated and enriched by Gene Ontology (GO) (http://www.geneontology.org, accessed on 10 May 2021) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (www.kegg.jp/kegg/pathway.html, accessed on 10 May 2021), respectively, to obtain the function and pathway results of DEGs, and the results were visualized by R packages [37,38].

2.3. Untargeted Metabolomics Analysis

Tea leaves were ground with liquid nitrogen (100 mg), homogenized with 100 µL, and suspended with precooled 100% methanol (−20 °C), and then swirled fully. Samples were kept at −20 °C for 60 min and centrifuged at 4 °C for 14,000× g for 15 min. Then, supernatants were transferred to a fresh microcentrifuge tube and dried in a vacuum evaporator. After drying, the metabolites were redissolved with 80% methanol and analyzed by the HPLC-MS/MS platform [39]. Next, the data were extracted using Compound Discover V3.1 (CD) software, including noise filtering, retention time alignment, mass spectrometry peak extraction, compound mapping between samples, compound identification, gap filling, and background subtracting. Meanwhile, compounds were identified using the mzCloud and ChemSpider database [40,41,42]. Finally, based on the quality control (QC) sample, it was filtered, and data standardization and normalization were achieved by the Constant SUM and Auto Scaling algorithm [43,44].

2.4. Metabolome Data Processing and Analysis

The standardized tea metabolomic data were analyzed by a principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), and visualized as scatter plots [45,46]. Differentially accumulated metabolites (DAMs) between the FS group and CK group were determined according to t-test p value < 0.05 and VIP > 1. Finally, the DAMs metabolic pathway was annotated and enriched based on the KEGG pathway database, and the results were visualized using R language.

3. Results

3.1. C. sinensis Transcriptome Sequencing Results and Data Analyses

The raw data of 251 G were obtained by Illumina high-throughput sequencing, and the clean data of 247 G were obtained by filtering, and the clean rate of each group of data was greater than 97.71% (Table S1). Q20 were all greater than 98%, and Q30 were all greater than 93%. The mapping ratio of clean data to reference genome was 89% to 90%, in which both uniquely mapped reads and multiple mapped reads were around 84% and 16%, respectively. The average transcript number of the FS group (70,616) was smaller than the CK group (73,232). In addition, the exon total length, average transcript length, and N50 length (without intron) of the FS group were also smaller than the CK group.

3.2. Transcriptional Characteristics of C. sinensis Response to Freezing Stress

The correlation analysis showed that the r values of transcriptome data within both FS and CK were greater than 0.9, and the r values between groups were about 0.85 (Figure 1A). Compared with CK, a total of 3880 DEGs were obtained under freezing stress, of which 1876 DEGs were up-regulated and 2004 DEGs were down-regulated (Figure 1B). The GO enrichment analysis of DEGs showed that DEGs had a large amount of enrichment in biological processes, cell components, and molecular functions (Figure 1C). In terms of biological processes, the number of expression genes in the metabolic process and the cellular process all exceeded 277, and the number of expression genes in the single-organism process was 172. Gene expression was all highly enriched in cell components including the membrane, membrane part, cell, cell part, and organelle. Gene expression was mainly concentrated in the molecular functions including catalytic activity and binding, and the number of gene expressions was close to 400. In addition, under freezing stress, C. sinensis also enriched significantly in the immune system process, responses to stimulus, antioxidant activities, and other stress reactions.

3.3. Pathway Analysis in Response to Freezing Stress

The KEGG enrichment analysis suggested that DEGs were mainly enriched in photosynthesis, sugar and flavonoid metabolism, and signal transduction (Figure 2A). Furthermore, the MAPK pathway played an essential role. The analysis of the MAPK signaling pathway under freezing stress showed that PYR/PYL-PP2C-SnRK2 had a positive response to abscisic acid (ABA) signal transduction (Figure 2B). The physiological changes of stress adaptation and stomatal closure were responded to by ABA signal transduction. A large number of DEGs was assigned to the ABA signal transduction pathway, among which PYR/PYL-PP2C-SnRK2 was very significant. In the PYR/PYL gene family, five genes were significantly up-regulated, and CSS0047272 and CSS0017736 were the most significant up-regulated with log(FC) value greater than four. Moreover, the PP2C gene family and SnRK2 gene family had one significantly down-regulated gene and one significantly up-regulated gene, respectively (Figure 2C).

3.4. Metabolic Characteristics of C. sinensis Response to Freezing Stress

A total of 10543 DAMs were obtained through the non-targeted metabolomic analysis of C. sinensis leaves under freezing stress, and 353 metabolites were assigned to 171 KEGG functional categories (Table S2 and Figure 3C). Freezing stress and CK had significantly different effects on the expression of metabolites, and great similarity within samples of the same condition. To further overview the distinctions between C. sinensis leaves under freezing stress and CK, the multivariate analysis was performed on metabolic data. A principal component analysis (PCA) of the 10,543 DAMs showed that the two treatments were significantly differentiated for the first component (79.3% for PC1) (Figure 3A). The OPLS-DA of the 10,543 DAMs showed a clearer distinction between the FS and CK groups of samples (Figure 3B). The R2X, R2Y, and Q2 of OPLS-DA model were 0.775, 1, and 0.996, respectively, with high reliability. A total of 353 metabolites were mapped to 170 pathways by KEGG annotation, in which DAMs were mainly enriched in diterpenoid biosynthesis, biosynthesis of secondary metabolites, flavone and flavonol biosynthesis, and other amino acid metabolic processes (Figure 3D).

3.5. Correlation Analysis of Gene Expression and Metabolite Levels

The KEGG annotation of DEGs and DAMs of freezing-stressed C. sinensis leaves showed that both sets of the data were significantly enriched in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis (Table S3, Figure 4A,B). Indeed, 32 DAMs were intensively enriched in the three connected pathways, of which 31 DAMs were up-regulated and only one down-regulated. All the DAMs in flavone and flavonol biosynthesis and flavonoid biosynthesis pathways were up-regulated. Moreover, 48 DEGs engaged in the pathway, and most of them were up-regulated. Alcohols and aldehydes produced in phenylpropanoid biosynthesis were the initial compounds for lignin formation [47]. Up-regulated Epigallocatechin was the most abundant flavonoidin in green tea and was an important potential source of antioxidants [48]. In addition, a number of flavonoid metabolites (luteolin, luteoloside, Gallocatechin, etc.) increased and were significantly associated with plant stress in the flavone and flavonol biosynthesis and flavonoid biosynthesis pathways [49].

4. Discussion

C. sinensis is a warm-loving crop, and its yield, quality, and even life will be seriously affected by the freezing injury. The technology of combining transcriptome and metabolome has been widely used in humans, animals, and plants, and played an essential role in the study of abiotic stresses in plants. In this study, the freezing-resistance mechanism of 60-year-old C. sinensis trees under natural freezing stress was studied using transcriptome and metabolome.
In transcriptome sequencing, the transcript number of the FS samples was smaller than that of the CK samples, and the number of down-regulated DEGs was higher than the number of up-regulated DEGs. Many of these down-regulated genes were associated with the normal development and physiological metabolism of C. sinensis, suggesting that at 10 °C C. sinensis recovered from the dormancy state to normal vegetative growth. The enrichment of DEGs was observed in the photosynthesis–antenna proteins pathway. Photosystem II (PSII) was one of the most sensitive components of photosynthesis. In addition to the reactive oxygen species (ROS) produced by freezing stress, the light-harvesting complex also produced ROS which could damage PSII and inhibit the photosynthesis of C. sinensis under freezing stress.
According to the GO enrichment analysis, the membrane was the most enriched cell structure of DEGs. Moreover, the biological processes (e.g., response to stimulus) and molecular functions (e.g., antioxidant activity and signal transducer activity) of GO enrichment were related to abiotic stresses and inseparable from the membrane. Cell membrane played a critical role in the response of C. sinensis to freezing stress. Through the analysis of DEGs pathways, the MAPK pathway and ABA signal transduction processes had a positive response to freezing stress in C. sinensis leaves. A large number of genes of PYR/PYL-PP2C-SnRK2, the core module of ABA signal transduction, were up-regulated to transmit the signal of freezing stress to promote C. sinensis and produce stress adaptation. Pyr/PYL-PP2C-SnRK2 contributed to stomata closure to improve the water holding capacity and freezing resistance of C. sinensis. The MAPK pathway process and ABA signal transduction mediated the information transmission in the freezing stress of C. sinensis. In addition, ABA can be used as a dormant hormone to promote leaf shedding [50]. C. sinensis metabolic pathways were also enriched in many material syntheses such as carbohydrates and flavonoids. Carbohydrates can provide energy for plants, and many flavonoids are associated with stress tolerance. These substances can also increase the concentration of cell fluid to improve freezing resistance [51]. C. sinensis could resist freezing stress through complex stress processes composed of stress signal transduction, substance synthesis, and physiological changes.
The pathway enrichment analysis of DAMs and DEGs exhibited positive responses to freezing stress in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways. DEGs regulated three pathways to produce 31 significantly up-regulated metabolites and to resist freezing stress in C. sinensis. Phenylalanine and cinnamic acid in the phenylpropanoid biosynthesis pathway were precursor substances for lignin synthesis, and the expression of regulatory genes CSS0018870 and CSS0021474 were significantly up-regulated, which were related to lignin synthesis. Lignin, the main component of the cell wall, can promote the transport of minerals in plants and improve the ability of water retention, and act as the first barrier for plants to resist adverse external environments to cope with stress [52]. C. sinensis responded to freezing stress by regulating the synthesis of substances through phenylpropanoid biosynthesis pathways. All DAMs in the flavone and flavonol biosynthesis and flavonoid biosynthesis pathways were significantly up-regulated. Flavonoids are one of the plant’s most biologically active secondary metabolites and have significant antioxidant activity [53]. Luteolin 7-O-glucoside, which mainly exists in vacuoles, can enhance the adaptive ability of plants [54]. Myricetin, a molecule with six hydroxyl groups, has antioxidant properties [55]. In addition, the reduction of flavonoids will reduce the freezing resistance of plants [56,57]. Flavonoids played an essential role in the freezing stress of C. sinensis.

5. Conclusions

In conclusion, PYR/PYL-PP2C-SnRK2 showed a positive response in the signal transduction of freezing stress in C. sinensis. In addition, the phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosyn-thesis pathways regulated by genes encoding enzymes might play a key role. In this experiment, large numbers of DEGs, metabolites, and key pathways were observed from C. sinensis under freezing stress. It is worth studying further how the freezing stress signals are conducted and how these significantly related candidate genes and metabolites regulate the freezing resistance of C. sinensis. The results laid a foundation for improving the freezing resistance of C. sinensis by genetic engineering.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/biology10100996/s1, Table S1. Basic information of C. sinensis transcriptome sequencing under freezing stress; Table S2. DAMs KEGG annotation results, Table S3: The DEGs of C. sinensis under freezing stress.

Author Contributions

Conceptualization, H.W., H.T., and L.Y.; Data curation, H.W., Y.W., Y.Z., H.T., and L.Y.; Formal analysis, H.W. and Y.W.; Funding acquisition, H.W., H.T., and L.Y.; Investigation, Z.W., H.T., and L.Y.; Methodology, H.W.; Project administration, Z.W., J.D., H.T., and L.Y.; Resources, H.W. and Y.W.; Software, H.W., Z.W., and Y.Z.; Supervision, J.D., Y.Z., H.T., and L.Y.; Validation, J.D., Y.Z., and L.Y.; Visualization, Y.W., Y.Z., and L.Y.; Writing—original draft, H.W. and L.Y.; Writing—review and editing, H.W. and L.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Foundation of Innovation Team Project for Modern Agricultural Industrious Technology System of Shandong Province (SDAIT-25-01) and the Foundation of Central Government Funds for Guiding Local Scientific and Technological Development (YDZX20203700001034). And we thank Supercomputing Center in Shandong Agricultural University for technical support.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Xia, E.; Tong, W.; Hou, Y.; An, Y.; Chen, L.; Wu, Q.; Liu, Y.; Yu, J.; Li, F.; Li, R.; et al. The reference genome of tea plant and resequencing of 81 diverse accessions provide insights into genome evolution and adaptation of tea plants. Mol. Plant. 2020, 13, 1013–1026. [Google Scholar] [CrossRef] [PubMed]
  2. Ahammed, G.J.; Li, X.; Liu, A.; Chen, S. Physiological and defense responses of tea plants to elevated CO2: A review. Front. Plant. Sci. 2020, 11, 305. [Google Scholar] [CrossRef]
  3. Karak, T.; Kutu, F.R.; Nath, J.R.; Sonar, I.; Paul, R.K.; Boruah, R.K.; Sanyal, S.; Sabhapondit, S.; Dutta, A.K. Micronutrients (B, Co, Cu, Fe, Mn, Mo, and Zn) content in made tea (Camellia sinensis L.) and tea infusion with health prospect: A critical review. Crit. Rev. Food Sci. Nutr. 2017, 57, 2996–3034. [Google Scholar] [CrossRef]
  4. Wang, X.; Feng, H.; Chang, Y.; Ma, C.; Wang, L.; Hao, X.; Li, A.; Cheng, H.; Wang, L.; Cui, P.; et al. Population sequencing enhances understanding of tea plant evolution. Nat. Commun. 2020, 11, 4447. [Google Scholar] [CrossRef]
  5. Bredow, M.; Walker, V.K. Ice-binding proteins in plants. Front. Plant. Sci. 2017, 8, 2153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Shi, Y.; Cai, Z.; Li, D.; Lu, J.; Ye, J.; Liang, Y.; Zheng, X. Effect of freezing on photosystem II and assessment of freezing tolerance of tea cultivar. Plants 2019, 8, 434. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Zheng, C.; Zhao, L.; Wang, Y.; Shen, J.; Zhang, Y.; Jia, S.; Li, Y.; Ding, Z. Integrated RNA-Seq and sRNA-Seq analysis identifies chilling and freezing responsive key molecular players and pathways in tea plant (Camellia sinensis). PLoS ONE 2015, 10, e0125031. [Google Scholar] [CrossRef] [PubMed]
  8. Charrier, G.; Ngao, J.; Saudreau, M.; Améglio, T. Effects of environmental factors and management practices on microclimate, winter physiology, and frost resistance in trees. Front. Plant. Sci. 2015, 6, 259. [Google Scholar] [CrossRef] [Green Version]
  9. Verslues, P.E.; Agarwal, M.; Katiyar-Agarwal, S.; Zhu, J.; Zhu, J.K. Methods and concepts in quantifying resistance to drought, salt and freezing, abiotic stresses that affect plant water status. Plant J. 2006, 45, 523–539. [Google Scholar] [CrossRef]
  10. Jaikumar, N.S.; Dorn, K.M.; Baas, D.; Wilke, B.; Kapp, C.; Snapp, S.S. Nucleic acid damage and DNA repair are affected by freezing stress in annual wheat (Triticum aestivum) and by plant age and freezing in its perennial relative (Thinopyrum intermedium). Am. J. Bot. 2020, 107, 1693–1709. [Google Scholar] [CrossRef]
  11. Lim, C.J.; Park, J.; Shen, M.; Park, H.J.; Cheong, M.S.; Park, K.S.; Baek, D.; Bae, M.J.; Ali, A.; Jan, M.; et al. The histone-modifying complex PWR/HOS15/HD2C epigenetically regulates cold tolerance. Plant. Physiol. 2020, 184, 1097–1111. [Google Scholar] [CrossRef]
  12. Sun, S.; Fang, J.; Lin, M.; Hu, C.; Qi, X.; Chen, J.; Zhong, Y.; Muhammad, A.; Li, Z.; Li, Y. Comparative metabolomic and transcriptomic studies reveal key metabolism pathways contributing to freezing tolerance under cold stress in kiwifruit. Front. Plant Sci. 2021, 12, 628969. [Google Scholar] [CrossRef]
  13. Lu, H.; Xue, L.; Cheng, J.; Yang, X.; Xie, H.; Song, X.; Qiang, S. Polyploidization-driven differentiation of freezing tolerance in Solidago Canadensis. Plant Cell Environ. 2020, 43, 1394–1403. [Google Scholar] [CrossRef]
  14. do Amaral, M.C.F.; Frisbie, J.; Crum, R.J.; Goldstein, D.L.; Krane, C.M. Hepatic transcriptome of the freeze-tolerant Cope’s gray treefrog, Dryophytes chrysoscelis: Responses to cold acclimation and freezing. BMC Genom. 2020, 21, 226. [Google Scholar] [CrossRef] [PubMed]
  15. Hajihashemi, S.; Brestic, M.; Landi, M.; Skalicky, M. Resistance of Fritillaria imperialis to freezing stress through gene expression, osmotic adjustment and antioxidants. Sci Rep. 2020, 10, 10427. [Google Scholar] [CrossRef]
  16. Wang, W.; Wang, X.; Lv, Z.; Khanzada, A.; Huang, M.; Cai, J.; Zhou, Q.; Huo, Z.; Jiang, D. Effects of cold and salicylic acid priming on free proline and sucrose accumulation in winter wheat underfreezing stress. J. Plant Growth Regul. 2021, 204, 1–14. [Google Scholar]
  17. Pu, Y.; Liu, L.; Wu, J.; Zhao, Y.; Bai, J.; Ma, L.; Yue, J.; Jin, J.; Niu, Z.; Fang, Y.; et al. Transcriptome profile analysis of winter rapeseed (Brassica napus L.) in response to freezing stress, reveal potentially connected events to freezing stress. Int. J. Mol. Sci. 2019, 20, 2771. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Li, T.; Wang, Y.H.; Liu, J.X.; Feng, K.; Xu, Z.S.; Xiong, A.S. Advances in genomic, transcriptomic, proteomic, and metabolomic approaches to study biotic stress in fruit crops. Crit. Rev. Biotechnol. 2019, 39, 680–692. [Google Scholar] [CrossRef] [PubMed]
  19. Xia, L.; Xiaodong, M.; Yunhe, C.; Junxiang, L.; Junzhu, Z.; Feifei, Z.; Zhenyuan, S.; Lei, H. Transcriptomic and metabolomic insights into the adaptive response of Salix viminalis to phenanthrene. Chemosphere 2021, 262, 127573. [Google Scholar] [CrossRef]
  20. Lin, S.; Zeng, S.; Yang, X.; Yang, T.; Zheng, G.; Mao, G.; Wang, Y. Integrative analysis of transcriptome and metabolome reveals salt stress orchestrating the accumulation of specialized metabolites in Lycium barbarum L. fruit. Int. J. Mol. Sci. 2021, 22, 4414. [Google Scholar] [CrossRef]
  21. Sharma, A.; Shahzad, B.; Rehman, A.; Bhardwaj, R.; Landi, M.; Zheng, B. Response of phenylpropanoid pathway and the role of polyphenols in plants under abiotic stress. Molecules 2019, 24, 2452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Li, Y.; Wang, X.; Ban, Q.; Zhu, X.; Jiang, C.; Wei, C.; Bennetzen, J.L. Comparative transcriptomic analysis reveals gene expression associated with cold adaptation in the tea plant Camellia sinensis. BMC Genom. 2019, 20, 624. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Samarina, L.S.; Bobrovskikh, A.V.; Doroshkov, A.V.; Malyukova, L.S.; Matskiv, A.O.; Rakhmangulov, R.S.; Koninskaya, N.G.; Malyarovskaya, V.I.; Tong, W.; Xia, E.; et al. Comparative expression analysis of stress-inducible candidate genes in response to cold and drought in tea plant [Camellia sinensis (L.) Kuntze]. Front. Genet. 2020, 11, 611283. [Google Scholar] [CrossRef]
  24. Lin, S.R.; Yu, S.Y.; Chang, T.D.; Lin, Y.J.; Wen, C.J.; Lin, Y.H. First report of anthracnose caused by Colletotrichum fructicola on tea in Taiwan. Plant. Dis. 2020, 3, 105. [Google Scholar] [CrossRef]
  25. Yao, L.; Ding, C.; Hao, X.; Zeng, J.; Yang, Y.; Wang, X.; Wang, L. CsSWEET1a and CsSWEET17 mediate growth and freezing tolerance by promoting sugar transport across the plasma membrane. Plant. Cell Physiol. 2020, 61, 1669–1682. [Google Scholar] [CrossRef] [PubMed]
  26. Passow, C.N.; Kono, T.J.Y.; Stahl, B.A.; Jaggard, J.B.; Keene, A.C.; McGaugh, S.E. Nonrandom RNAseq gene expression associated with RNAlater and flash freezing storage methods. Mol. Ecol. Resour. 2019, 19, 456–464. [Google Scholar] [CrossRef]
  27. Palomares, M.A.; Dalmasso, C.; Bonnet, E.; Derbois, C.; Brohard-Julien, S.; Ambroise, C.; Battail, C.; Deleuze, J.F.; Olaso, R. Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples. Sci. Rep. 2019, 9, 7550. [Google Scholar] [CrossRef] [Green Version]
  28. Wu, H.; Li, H.; Zhang, W.; Tang, H.; Yang, L. Transcriptional regulation and functional analysis of Nicotiana tabacum under salt and ABA stress. Biochem. Biophys. Res. Commun. 2021, 570, 110–116. [Google Scholar] [CrossRef]
  29. Soler-García, A.A.; De Jesús, A.J.; Taylor, K.; Brown, E.W. Differentiation of Salmonella strains from the SARA, SARB and SARC reference collections by using three genes PCR-RFLP and the 2100 Agilent Bioanalyzer. Front. Microbiol. 2014, 5, 417. [Google Scholar]
  30. Holm, J.B.; Humphrys, M.S.; Robinson, C.K.; Settles, M.L.; Ott, S.; Fu, L.; Yang, H.; Gajer, P.; He, X.; McComb, E.; et al. Ultrahigh-throughput multiplexing and sequencing of > 500-base-pair amplicon regions on the Illumina HiSeq 2500 platform. mSystems 2019, 4, e00029-19. [Google Scholar] [CrossRef] [Green Version]
  31. Hashmi, J.A.; Al-Harbi, K.M.; Ramzan, K.; Albalawi, A.M.; Mehmood, A.; Samman, M.I.; Basit, S. A novel splice-site mutation in the ASPM gene underlies autosomal recessive primary microcephaly. Ann. Saudi Med. 2016, 36, 391–396. [Google Scholar] [CrossRef] [Green Version]
  32. Ward, C.M.; To, T.H.; Pederson, S.M. ngsReports: A Bioconductor package for managing FastQC reports and other NGS related log files. Bioinformatics 2020, 36, 2587–2588. [Google Scholar] [CrossRef]
  33. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef] [PubMed]
  34. Kim, D.; Paggi, J.M.; Park, C.; Bennett, C.; Salzberg, S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019, 37, 907–915. [Google Scholar] [CrossRef] [PubMed]
  35. Pertea, M.; Kim, D.; Pertea, G.M.; Leek, J.T.; Salzberg, S.L. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat. Protoc. 2016, 11, 1650–1667. [Google Scholar] [CrossRef] [PubMed]
  36. Shi, J.; Zhang, P.; Su, H.; Cai, L.; Zhao, L.; Zhou, H. Bioinformatics analysis of neuroblastoma miRNA based on GEO data. Pharmgenomics Pers. Med. 2021, 14, 849–858. [Google Scholar] [PubMed]
  37. Harris, M.A.; Clark, J.; Ireland, A.; Lomax, J.; Ashburner, M.; Foulger, R.; Eilbeck, K.; Lewis, S.; Marshall, B.; Mungall, C.; et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 2004, 32, D258–D261. [Google Scholar]
  38. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef] [Green Version]
  39. Angeloni, S.; Navarini, L.; Khamitova, G.; Maggi, F.; Sagratini, G.; Vittori, S.; Caprioli, G. A new analytical method for the simultaneous quantification of isoflavones and lignans in 25 green coffee samples by HPLC-MS/MS. Food Chem. 2020, 325, 126924. [Google Scholar] [CrossRef] [PubMed]
  40. Aso, Y.; Sano, M.; Kuroda, H.; Ohara, H.; Ando, H.; Matsumoto, K. DISCOVER: A facile structure-based screening method for vinyl compound producing microbes. Sci. Rep. 2019, 9, 16007. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Wang, M.R.; Chen, R.J.; Zhao, F.; Zhang, H.H.; Bi, Q.Y.; Zhang, Y.N.; Zhang, Y.Q.; Wu, Z.C.; Ji, X.M. Effect of Wenxia Changfu formula combined with cisplatin reversing non-small cell lung cancer cell adhesion-mediated drug resistance. Front. Pharmacol. 2020, 11, 500137. [Google Scholar] [CrossRef]
  42. Ng, B.; Quinete, N.; Gardinali, P.R. Assessing accuracy, precision and selectivity using quality controls for non-targeted analysis. Sci. Total Environ. 2020, 713, 136568. [Google Scholar] [CrossRef]
  43. Skedgel, C.; Regier, D.A. Constant-sum paired comparisons for eliciting stated preferences: A tutorial. Patient 2015, 8, 155–163. [Google Scholar] [CrossRef]
  44. Webb-Robertson, B.J.; Lowry, D.F.; Jarman, K.H.; Harbo, S.J.; Meng, Q.R.; Fuciarelli, A.F.; Pounds, J.G.; Lee, K.M. A study of spectral integration and normalization in NMR-based metabonomic analyses. J. Pharm. Biomed. Anal. 2005, 39, 830–836. [Google Scholar] [CrossRef]
  45. Mi, J.X.; Zhang, Y.N.; Lai, Z.; Li, W.; Zhou, L.; Zhong, F. Principal component analysis based on nuclear norm minimization. Neural Netw. 2019, 118, 1–16. [Google Scholar] [CrossRef]
  46. Yang, Q.; Tian, G.L.; Qin, J.W.; Wu, B.Q.; Tan, L.; Xu, L.; Wu, S.Z.; Yang, J.T.; Jiang, J.H.; Yu, R.Q. Coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis: Stable metabolic biomarker selection for inherited metabolic diseases. Talanta 2020, 219, 121370. [Google Scholar] [CrossRef]
  47. Vogt, T. Phenylpropanoid biosynthesis. Mol. Plant 2010, 3, 2–20. [Google Scholar] [CrossRef] [Green Version]
  48. Ambigaipalan, P.; Oh, W.Y.; Shahidi, F. Epigallocatechin (EGC) esters as potential sources of antioxidants. Food Chem. 2020, 309, 125609. [Google Scholar] [CrossRef] [PubMed]
  49. Wen, W.; Alseekh, S.; Fernie, A.R. Conservation and diversification of flavonoid metabolism in the plant kingdom. Curr. Opin. Plant. Biol. 2020, 55, 100–108. [Google Scholar] [CrossRef] [PubMed]
  50. Rubio, S.; Noriega, X.; Pérez, F.J. Abscisic acid (ABA) and low temperatures synergistically increase the expression of CBF/DREB1 transcription factors and cold-hardiness in grapevine dormant buds. Ann. Bot. 2019, 123, 681–689. [Google Scholar] [CrossRef] [PubMed]
  51. Wang, Y.; Xiong, F.; Nong, S.; Liao, J.; Xing, A.; Shen, Q.; Ma, Y.; Fang, W.; Zhu, X. Effects of nitric oxide on the GABA, polyamines, and proline in tea (Camellia sinensis) roots under cold stress. Sci. Rep. 2020, 10, 12240. [Google Scholar] [CrossRef]
  52. Liu, Q.; Luo, L.; Zheng, L. Lignins: Biosynthesis and biological functions in plants. Int. J. Mol. Sci. 2018, 19, 335. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Wang, W.; Li, Y.; Dang, P.; Zhao, S.; Lai, D.; Zhou, L. Rice secondary metabolites: Structures, roles, biosynthesis, and metabolic regulation. Molecules. 2018, 23, 3098. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Böttner, L.; Grabe, V.; Gablenz, S.; Böhme, N.; Appenroth, K.J.; Gershenzon, J.; Huber, M. Differential localization of flavonoid glucosides in an aquatic plant implicates different functions under abiotic stress. Plant Cell Environ. 2021, 44, 900–914. [Google Scholar] [CrossRef]
  55. Knez Hrnčič, M.; Ivanovski, M.; Cör, D.; Knez, Ž. Chia Seeds (Salvia hispanica L.): An overview-phytochemical profile, isolation methods, and application. Molecules 2019, 25, 11. [Google Scholar] [CrossRef] [Green Version]
  56. Schulz, E.; Tohge, T.; Zuther, E.; Fernie, A.R.; Hincha, D.K. Flavonoids are determinants of freezing tolerance and cold acclimation in Arabidopsis thaliana. Sci. Rep. 2016, 6, 34027. [Google Scholar] [CrossRef] [PubMed]
  57. Zhao, M.; Jin, J.; Gao, T.; Zhang, N.; Jing, T.; Wang, J.; Ban, Q.; Schwab, W.; Song, C. Glucosyltransferase CsUGT78A14 regulates flavonols accumulation and reactive oxygen species scavenging in response to cold stress in Camellia sinensis. Front. Plant. Sci. 2019, 10, 1675. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Correlation analyses between the gene expression of samples under control and freezing stress conditions. (A) Correlations between different samples. (B) Volcano plot of DEGs identified under freezing stress. Red dots represent up-regulated DEGs and blue dots represent down-regulated DEGs. (C) GO annotation results for the respective DEGs.
Figure 1. Correlation analyses between the gene expression of samples under control and freezing stress conditions. (A) Correlations between different samples. (B) Volcano plot of DEGs identified under freezing stress. Red dots represent up-regulated DEGs and blue dots represent down-regulated DEGs. (C) GO annotation results for the respective DEGs.
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Figure 2. The pathway analysis of DEGs. (A) Bubble plot of the results of DEGs KEGG annotation. (B) Regulation of ABA signal transduction pathway under freezing stress. (C) Gene expression of ABA signal transduction pathway after freezing stress.
Figure 2. The pathway analysis of DEGs. (A) Bubble plot of the results of DEGs KEGG annotation. (B) Regulation of ABA signal transduction pathway under freezing stress. (C) Gene expression of ABA signal transduction pathway after freezing stress.
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Figure 3. The quality and functional analysis of C. sinensis metabolome data. (A) The scatter plot of PCA scores of C. sinensis samples under freezing stress and CK. (B) OPLS-DA modeling of C. sinensis samples under freezing stress and CK. (C) The DAMs cluster heat map between samples. (D) The enrichment analysis of KEGG pathway top 10 for DAMs in C. sinensis.
Figure 3. The quality and functional analysis of C. sinensis metabolome data. (A) The scatter plot of PCA scores of C. sinensis samples under freezing stress and CK. (B) OPLS-DA modeling of C. sinensis samples under freezing stress and CK. (C) The DAMs cluster heat map between samples. (D) The enrichment analysis of KEGG pathway top 10 for DAMs in C. sinensis.
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Figure 4. Gene expression regulates substance metabolism. (A) The enrichment analysis of regulatory genes encoding enzymes and DAMs in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways. The box represents metabolites, red represents up-regulated metabolites, and blue represents down-regulated metabolites. Between the metabolites are regulatory respective genes encoding enzymes. (B) Expression of genes involved in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways of freezing stress.
Figure 4. Gene expression regulates substance metabolism. (A) The enrichment analysis of regulatory genes encoding enzymes and DAMs in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways. The box represents metabolites, red represents up-regulated metabolites, and blue represents down-regulated metabolites. Between the metabolites are regulatory respective genes encoding enzymes. (B) Expression of genes involved in phenylpropanoid biosynthesis, flavone and flavonol biosynthesis, and flavonoid biosynthesis pathways of freezing stress.
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Wu, H.; Wu, Z.; Wang, Y.; Ding, J.; Zheng, Y.; Tang, H.; Yang, L. Transcriptome and Metabolome Analysis Revealed the Freezing Resistance Mechanism in 60-Year-Old Overwintering Camellia sinensis. Biology 2021, 10, 996. https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100996

AMA Style

Wu H, Wu Z, Wang Y, Ding J, Zheng Y, Tang H, Yang L. Transcriptome and Metabolome Analysis Revealed the Freezing Resistance Mechanism in 60-Year-Old Overwintering Camellia sinensis. Biology. 2021; 10(10):996. https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100996

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Wu, Hui, Zixian Wu, Yuanheng Wang, Jie Ding, Yalin Zheng, Heng Tang, and Long Yang. 2021. "Transcriptome and Metabolome Analysis Revealed the Freezing Resistance Mechanism in 60-Year-Old Overwintering Camellia sinensis" Biology 10, no. 10: 996. https://0-doi-org.brum.beds.ac.uk/10.3390/biology10100996

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