1 Introduction

Tea (Camellia sinensis) is one of the most widely consumed beverages globally, with a long-standing cultural heritage and immense economic importance. As a major cash crop in countries such as China, India, Sri Lanka, Kenya, and Japan, tea supports the livelihoods of millions of smallholder farmers and plays a crucial role in international trade. According to the Food and Agriculture Organization (FAO), global tea production has continued to rise steadily, driven by increasing consumer demand for high-quality and health-promoting beverages (Wang et al., 2021; Moreira et al., 2024).

The quality of tea is established by a complex array of properties, which include flavor, aroma, taste, appearance, and biochemical composition. Such properties are influenced by both genetic and environmental factors and are buttressed by highly advanced metabolic activities involving polyphenols (e.g., catechins and theaflavins), amino acids (in particular, theanine), caffeine, and a wide variety of volatile organic compounds (VOCs) (Kong et al., 2025). The sensory characteristics of tea—bitterness, astringency, umami, and roasty or floral aromas—result from highly controlled interactions among these compounds, which are differentially expressed in tea cultivars, cultivation conditions, and processing. An understanding of the genetic regulation of these quality traits is valuable to both basic and applied research. With next-generation sequencing (NGS), transcriptome profiling, metabolomics, and genome editing technologies, researchers have at their command the unparalleled tools to dissect the molecular foundation of tea quality. However, as information on single biosynthetic genes and pathways is being accumulated, the genetic networks that control trait integration and coordination are still largely unresolved. This has presented a challenge to the development of molecular markers and precise methods of improving tea quality through breeding (Xia et al., 2020).

This study provides a comprehensive synthesis of recent advances in uncovering the genetic and regulatory networks that control tea quality traits, including the identification of key functional genes, transcriptional and epigenetic regulators, and multi-omics-based network construction. It also highlights the practical potential of integrating systems biology approaches with molecular breeding technologies—such as marker-assisted selection, quantitative trait loci (QTL) mapping, and CRISPR-based genome editing—to accelerate the development of high-quality tea cultivars. A deeper understanding of these genetic networks not only enhances knowledge of trait evolution and diversification in Camellia sinensis, but also lays a solid foundation for precision breeding and the sustainable improvement of global tea quality.

2 Classification and Phenotypic Characteristics of Tea Quality Traits

2.1 Quality traits related to chemical composition

The tea quality is established essentially by the chemical composition of tea, and this contains amino acids, catechins, flavonoids, phenolic acids, caffeine, and other volatile compounds. These metabolites create color, taste, and flavor in tea. Amino acids, for example, contribute to brightness and fresh-sweet tastes, while catechins contribute bitterness and astringency. Specific metabolites such as 4-hydroxybenzoyl glucose and feruloyl quinic acid control color luminance, whereas volatile compounds such as linalool and 1-octen-3-ol control flower and fruity scents. The processing and storage transformation and equilibrium of such a compound dictate the final quality of tea products (Fan et al., 2021; Fan et al., 2022; Guo et al., 2023).

2.2 Sensory quality attributes

Sensory characteristics of tea are appearance, aroma, taste and mouthfeel. Traditionally, these are determined by expert sensory panels, but objective determination utilizing sophisticated analytical technology has more recently been used. Sensory quality is regarded as being closely related to chemical make-up: sweetness and umami are enhanced by high soluble sugar and amino acid levels and reduced by low caffeine and polyphenol levels responsible for bitterness and astringency. Aroma characteristics result from a sophisticated mixture of volatile compounds imparting floral, fruity, chestnut, or woody notes according to their composition. Instrumental methods such as near-infrared spectroscopy (NIRS) and metabolomics are nowadays utilized broadly for determining quickly and objectively sensory features. For example, in Enshi Yulu tea, sensory assessment not only includes visual inspection of different samples (Figure 1A) but also a qualitative inspection of fragrance, flavor, and the total mouthfeel scores (Figure 1B) with close agreement of sensory characteristics and quality factors (Wang et al., 2022; Guo et al., 2023; Lu et al., 2023).

2.3 Influence of environmental and management factors

Sensory quality and chemical composition are determined by environmental conditions (altitude, soil, climate) and cultivation practice (methods of processing, storage, cultivar selection). For example, teas grown at high altitude are sweeter and fresher due to lower temperature and unique ecological conditions. Processing operations such as fermentation, withering, and yellowing alter the composition of major metabolites, therefore impacting taste and aroma. Soil properties and microbial population on the other hand are mainly accountable for the accrual of quality traits in tea leaves, and seasonal differences can cause significant changes in the amount of catechin, amino acids, and caffeine. All these combine to determine the regional and seasonal typic of the tea quality (Zhou et al., 2020; Yang et al., 2022; Wu et al., 2025).

3 Key Functional Genes and Metabolic Pathways Regulating Tea Quality

3.1 Genes involved in the biosynthesis of tea polyphenols and catechins

Catechins and other polyphenols are major contributors to tea’s taste and health benefits. Their biosynthesis is controlled by structural genes such as chalcone synthase, flavonoid 3',5'-hydroxylase, leucoanthocyanidin dioxygenase, and polyphenol oxidase. MYB transcription factors (e.g., CsMYB8, CsMYB99) play central roles in regulating these pathways. Co-expression network analyses have identified hub genes and modules that coordinate catechin biosynthesis, and environmental factors like light also influence gene expression and metabolite accumulation (Tai et al., 2018; Lu et al., 2024).

3.2 Metabolic pathways related to amino acid synthesis

Amino acids, particularly theanine, are responsible for tea's umami flavor. The genes like theanine synthetase and glutamine synthetase regulate the biosynthesis of theanine, being controlled by transcription factors like CsMYB9 and CsMYB49. WRKY transcription factors like CsWRKY53 and CsWRKY40 and abscisic acid (ABA) signaling play a significant role in theanine hydrolysis during postharvest treatment and influence the quality of finished tea. DNA methylation and other epigenetic mechanisms also control amino acid biosynthetic gene expression consistent with seasonal and environmental fluctuations (Qiao et al., 2019; Su et al., 2020; Li et al., 2023).

3.3 Genes involved in caffeine biosynthesis and degradation

Caffeine content is determined by N-methyltransferase genes, which have expanded in tea. MYB transcription factors (e.g., CsMYB85, CsMYB86) are involved in caffeine biosynthesis regulation. Comparative genomics shows that tea’s caffeine pathway evolved independently from those in coffee and cacao, with higher expression of caffeine biosynthetic genes correlating with increased caffeine accumulation in certain cultivars (Su et al., 2020).

3.4 Biosynthetic pathways of aromatic compounds

Aroma is shaped by the biosynthesis of volatile terpenoids, fatty acid-derived volatiles, and carotenoid-derived volatiles. Key genes include terpene synthases, carotenoid cleavage dioxygenases (CsCCD), and various glycosidases. MYB transcription factors (e.g., CsMYB68, CsMYB147) regulate the production of mono- and sesquiterpenoid volatiles. Alternative splicing and post-transcriptional regulation also play significant roles in modulating aroma compound biosynthesis during tea processing, such as withering and supplementary light exposure (Zhang et al., 2022; Ni et al., 2023; Lu et al., 2024).

4 Roles of Epigenetic Regulation and Transcription Factors in Quality Formation

4.1 Transcriptional regulatory networks

Transcription factors (TFs) such as MYB, bHLH, WRKY, and GOLDEN 2-LIKE (GLK) play important roles in regulating the biosynthesis of prominent secondary metabolites (e.g., catechins, theanine, caffeine, flavonoids, and aroma compounds) to determine tea quality. For example, MYB TFs regulate flavonoid, caffeine, and theanine biosynthesis, while WRKY TFs can act as negative regulators of O-methylated catechin biosynthesis. The bHLH family is involved in trichome development and influencing resistance and quality (Qiao et al., 2019). Dynamic gene regulatory networks, especially wound- or environment-stimulated networks, organize the expression of biosynthetic genes and TFs, as noted by the quick transcriptional reprogramming in oolong tea production and upon UV-B light exposure (Cheng et al., 2022; Zheng et al., 2022) (Figure 2).

4.2 Multi-layered regulation involving microRNAs and lncRNAs

Although the provided papers do not directly address the roles of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in tea quality formation, alternative splicing is highlighted as a key post-transcriptional regulatory mechanism. Alternative splicing events affect a significant proportion of flavor-related genes and TFs, especially during withering, and are closely correlated with changes in aroma compound accumulation. This suggests that multi-layered regulation beyond transcription, including RNA processing, is important for fine-tuning tea quality traits (Gu et al., 2022; Liu et al., 2023).

4.3 Epigenetic modifications and heritable variation

Epigenetic marks such as DNA methylation and histone acetylation play critical roles in the regulation of biosynthesis of secondary metabolites in tea. The degree of DNA methylation in promoter regions may regulate TF (e.g., CsMYC2a) binding to the main biosynthetic genes and, therefore, the accumulation of compounds with aromas such as indole. Histone acetylation and methylation regulate the expression of genes for aroma and hormone biosynthesis, especially under stress or postharvest treatment. Erasable and heritable DNA methylation patterns are associated with seasonally varying secondary metabolisms with effects on flavonoid and theanine pathway gene expression and TFs. These epigenetic marks represent heritable sources of tea quality variation and exhibit putative targets for breeding (Han et al., 2024; Zheng et al., 2024).

5 Applications of Multi-Omics Integration in Tea Quality Research

5.1 Integration of genomics and transcriptomics to analyze key gene expression patterns

Integrating genomics and transcriptomics enables the identification and functional analysis of genes involved in tea quality traits, such as polyphenol biosynthesis. This approach allows researchers to map gene expression patterns across developmental stages and processing conditions, revealing regulatory networks that underlie the accumulation of key metabolites in tea leaves. Such integration has been pivotal in understanding the genetic basis of polyphenol formation and changes during tea plant growth and processing (Zhang et al., 2020).

5.2 Metabolomics to reveal dynamic changes in secondary metabolism

Metabolomics provides a comprehensive profile of secondary metabolites, including catechins, theanine, and aroma compounds, during tea plant development and processing. By tracking dynamic changes in metabolite levels, metabolomics helps elucidate the biochemical pathways and environmental factors influencing tea quality. When combined with transcriptomics, this approach links gene expression with metabolite accumulation, offering insights into the regulation of tea flavor and health-related compounds (Yang et al., 2021).

5.3 Roles of proteomics and epigenomics in regulatory network construction

Proteomics complements transcriptomics by identifying and quantifying proteins that directly mediate metabolic processes, while epigenomics uncovers regulatory mechanisms such as DNA methylation and histone modification that affect gene expression. Together, these omics layers contribute to the construction of comprehensive regulatory networks governing tea quality formation, providing a holistic view of the molecular mechanisms involved.

5.4 Case study: Integrated omics approaches revealing regulatory pathways of theanine or aroma compounds

In albino tea plants, integrated genomics, transcriptomics, and metabolomics have been used to dissect the regulatory pathways of theanine and catechin accumulation. These studies have identified key genes, enzymes, and metabolites involved in the unique flavor profile of albino tea, demonstrating the power of multi-omics approaches in uncovering the molecular basis of tea quality traits (Zhang et al., 2020).

6 Construction of Genetic Regulatory Networks and Systems Biology Analyses of Tea Quality Traits

6.1 Network construction methods: WGCNA, Bayesian networks, machine learning, etc.

Weighted Gene Co-expression Network Analysis or WGCNA is widely used to identify gene modules and regulatory interactions in tea, especially in secondary metabolite pathways including catechins, theanine, and caffeine. WGCNA enables one to cluster gene expression data into modules with certain biological functions and identify hub genes. Bayesian machine learning techniques and clustering methods, such as genome-wide association studies (GWAS) and genomic prediction models, are also used to study population structure, trait association, and breeding values for quality traits (Zheng et al., 2022).

6.2 Identification of network modules and core regulatory factors

Network analyses have revealed multiple co-expression modules significantly associated with tea quality traits. For example, WGCNA identified 35 modules, with 20 linked to catechin, theanine, and caffeine biosynthesis. Hub genes and transcription factors (e.g., MADS, WRKY, SBP) within these modules act as core regulators. Integrative approaches combining transcriptomics and metabolomics further clarify the roles of these core factors in controlling metabolite accumulation and quality variation (Tai et al., 2018; Zheng et al., 2020).

6.3 Network visualization and functional enrichment analysis

Visualization tools and databases, such as TeaCoN, facilitate the exploration of gene co-expression networks, module relationships, and hub gene connectivity. Functional enrichment analyses (e.g., GO and KEGG) are routinely used to interpret the biological significance of network modules, revealing enrichment in pathways related to secondary metabolism, stress response, and development. These analyses help prioritize candidate genes for functional studies and breeding (Zhang et al., 2020).

6.4 Tea-specific regulatory patterns and variation among germplasms

Comparative network analyses across diverse tea cultivars and germplasms reveal both conserved and rewired regulatory modules, especially for secondary metabolism and environmental adaptation. SNP genotyping and GWAS have identified trait-linked polymorphisms and population structure differences, highlighting the genetic diversity underlying tea quality. These findings support the use of network-guided breeding and marker-assisted selection to improve tea quality traits.

7 Molecular Breeding Strategies and Future Perspectives

7.1 Development of quality-related molecular markers and QTL mapping

Advances in genomics and high-throughput sequencing have enabled the development of diverse molecular markers, such as SNPs and indels, for tea quality traits. QTL mapping and genome-wide association studies (GWAS) have identified candidate genes and loci associated with key metabolites like free amino acids and polyphenols, providing a foundation for marker-assisted selection (MAS) and accelerating the breeding of high-quality tea cultivars (Wang et al., 2024).

7.2 Potential of gene editing in improving quality traits

While direct applications of CRISPR in tea are still emerging, future prospects highlight gene editing as a promising tool for precise modification of quality-related genes. The integration of gene editing with multi-omics and molecular marker technologies is expected to enable targeted improvement of flavor, stress resistance, and other desirable traits (Lubanga et al., 2021).

7.3 Utilization of genetic diversity and identification of elite resources

Comprehensive germplasm characterization using molecular markers and pangenome analyses has revealed extensive genetic diversity in tea. This diversity is crucial for identifying elite resources and broadening the genetic base for breeding programs. Studies have shown that genetic divergence is not strictly linked to geographic origin, emphasizing the importance of systematic evaluation and utilization of diverse germplasm (Wang et al., 2024).

7.4 Establishing a precision breeding system focusing on quality traits

Genomic selection (GS) and genomics-assisted breeding strategies are being implemented to increase genetic gain, reduce breeding cycles, and enhance selection accuracy for complex quality traits. Integrating MAS, GS, and high-throughput phenotyping forms the basis of a precision breeding system tailored to tea quality improvement (Lubanga et al., 2022).

7.5 Current research limitations and future directions

Despite significant progress, challenges remain, including the long generation time of tea, limited functional validation of candidate genes, and the need for more efficient transformation and gene editing systems. Future research should focus on integrating single-cell omics, pangenomics, and advanced gene editing, as well as leveraging plant-microbe interactions and epigenetic regulation to further accelerate tea quality improvement (Xia et al., 2020).

8 Concluding Remarks

The recent advances in tea genomics have significantly enhanced our understanding of the genetic networks regulating quality characters such as flavor, aroma, and secondary metabolite composition. Precise pangenome assemblies and genome-wide association studies (GWAS) have identified numerous allelic variants and candidate genes connected with useful phenotypes, which include bud flush date, leaf color, and catechin, theanine, and caffeine biosynthesis. These findings provide a sound foundation for the explanation of tea quality's complex genetic composition and for the development of molecular markers to guide breeding programs.

The integration of multi-omics approaches—encompassing genomics, transcriptomics, metabolomics, and epigenomics—has enabled the construction of gene co-expression networks and the identification of hub genes and regulatory modules. Weighted gene co-expression network analysis (WGCNA) and other systems biology tools have revealed coordinated regulation among secondary metabolic pathways and highlighted the influence of environmental factors, such as light and stress, on metabolite accumulation. These systems-level insights are essential for understanding the dynamic and interconnected nature of tea quality trait regulation.

Unlocking the genetic networks controlling tea quality traits paves the way for precision molecular breeding. The application of genomic selection, marker-assisted selection, and gene editing technologies' potential to enhance the effectiveness and accuracy of breeding high-quality tea varieties. As more comprehensive multi-omics data become increasingly available and candidate genes are functionally validated, the prospects of breeding elite tea cultivars with improved quality traits will be further enhanced. Ultimately, these advances will enable the sustainable improvement of tea quality and world competitiveness of the tea industry.

Acknowledgments

The authors sincerely thank Dr. Wang for reviewing the manuscript and providing valuable suggestions, which contributed to its improvement. Additionally, heartfelt gratitude is extended to the two anonymous peer reviewers for their comprehensive evaluation of the manuscript. 

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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