Journal of Tea Science Research, 2024, Vol.14, No.6, 335-343 http://hortherbpublisher.com/index.php/jtsr 340 6 Applications of Multi-Omics Approaches in Flavonoid Research 6.1 Genomics and transcriptomics for key gene discovery Genomics and transcriptomics enable identification of genes and regulatory elements controlling flavonoid biosynthesis. High-throughput sequencing and transcriptome profiling yield gene expression profiles, unveiling candidate genes and transcription factors modulating flavonoid pathways. These approaches are pivotal to mapping biosynthetic networks and understanding genetic variation underlying flavonoid diversity (Subramanian et al., 2020; Wörheide et al., 2021). 6.2 Integration of metabolomics and proteomics to reveal metabolic flow and regulation points Metabolomics provides a comprehensive profile of flavonoid compounds, while proteomics identifies and quantifies enzymes and regulatory proteins. Integrating these datasets allows researchers to trace metabolic flux, pinpoint regulatory bottlenecks, and link gene expression to metabolite accumulation. For example, multi-omics analysis in genetically engineered plants has shown how flavonoid accumulation can impact precursor availability and alter cellular metabolism (Meng et al., 2020; Wörheide et al., 2021). 6.3 Multi-omics integration for network modeling and function prediction Combining genomics, transcriptomics, proteomics, and metabolomics enables the construction of detailed molecular networks. Advanced integration methods, including data-driven and knowledge-based approaches, facilitate the prediction of gene function, regulatory interactions, and pathway dynamics. These models help identify key regulatory nodes and potential targets for metabolic engineering (Subramanian et al., 2020; Vandereyken et al., 2023). 6.4 Case studies: Omics-driven insights into differential flavonoid accumulation Case studies using multi-omics approaches have provided insights into the mechanisms underlying differential flavonoid accumulation. For instance, integrated omics analysis in transgenic tomato revealed that increased flavonoid production can deplete precursor pools and affect other metabolic pathways, highlighting the interconnectedness of metabolic networks (Meng et al., 2020). Such studies demonstrate the power of multi-omics to uncover complex regulatory relationships and guide targeted interventions. 7 Molecular Breeding and Applied Prospects 7.1 Development of QTLs and molecular markers related to flavonoids Advances in genomics and transcriptomics have enabled the identification of quantitative trait loci (QTLs) and molecular markers associated with flavonoid content and composition. These tools facilitate marker-assisted selection and accelerate breeding for high-flavonoid cultivars. The integration of omics data has improved the mapping of genetic regions influencing flavonoid biosynthesis, supporting the development of new varieties with enhanced nutritional and functional properties (D’Amelia et al., 2018; Zhang et al., 2019). 7.2 Potential of gene editing (e.g., CRISPR) in quality trait improvement Gene editing technologies, such as CRISPR/Cas9, offer precise tools for modifying key genes in flavonoid biosynthetic pathways. These approaches enable targeted enhancement or suppression of specific flavonoid compounds, improving plant quality traits, stress tolerance, and health benefits. Synthetic biology and metabolic engineering further expand the potential for producing novel or high-value flavonoids in both plants and microbial systems (Nabavi et al., 2018; Wang et al., 2024). 7.3 Targeted breeding strategies for functional tea product development Targeted breeding strategies, informed by molecular markers and functional genomics, allow for the development of tea cultivars with tailored flavonoid profiles. These strategies support the creation of functional tea products with specific health benefits, improved taste, and enhanced stress resilience. The use of transcriptome and metabolome analyses helps identify candidate genes and regulatory elements for breeding programs (Chen et al., 2025; Lee et al., 2025).
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