Medicinal Plant Research 2024, Vol.14, No.2, 97-106 http://hortherbpublisher.com/index.php/mpr 103 al., 2022). Additionally, the quality and yield of L. japonica are significantly affected by environmental stressors, such as salt stress, which complicates breeding efforts aimed at enhancing these traits (Cai et al., 2021). Another challenge is the limited genetic diversity available for breeding programs, which restricts the potential for developing new varieties with improved characteristics. Figure 2 Overview of the metabolomics and transcriptomics workflow (Adopted from Li et al., 2022) Image caption: Lonicera japonica flowers (LJFs) at the silver flowering stage of two contrasting varieties of L. japonica, ‘Yujin2’ and ‘Fengjin1’ were collected. Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and transcriptomics analysis were performed for volatile organic compound (VOC) profiling and global gene expression patterns, respectively. Integrated metabolomics and transcriptomics analysis serves to elucidate the regulatory mechanism of floral scents of LJFs by bioinformatic analyses (Adopted from Li et al., 2022) 7.2 Potential breakthroughs in breeding strategies Recent advancements in omics technologies, such as metabolomics and transcriptomics, offer promising avenues for overcoming some of the current limitations in L. japonica breeding. Integrated analyses of volatile metabolites and gene expression have provided insights into the metabolic pathways and regulatory genes responsible for floral scent production, which could be targeted in future breeding programs to develop more fragrant varieties (Li et al., 2022). Polyploid breeding is another promising strategy that has been explored to enhance the yield and quality of L. japonica. This approach involves inducing polyploidy to create plants with multiple sets of chromosomes, which can result in increased biomass and improved stress tolerance. Additionally, the application of advanced statistical methods, such as partial least squares discriminant analysis (PLS-DA), can help identify
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