Journal of Tea Science Research, 2024, Vol.14, No.6, 304-312 http://hortherbpublisher.com/index.php/jtsr 308 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).
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