Plant Gene and Traits 2024, Vol.15, No.4, 207-219 http://genbreedpublisher.com/index.php/pgt 209 also unveiled the deep phylogeny and secondary metabolite evolution within the genus, highlighting the accumulation of metabolites like catechins and caffeine in specific sections (Wu et al., 2022). Metabolomic studies on diverse Camellia sinensis populations have identified signature metabolites associated with different phylogenetic groups, providing insights into the genetic and metabolite diversity useful for breeding programs (Yu et al., 2020). Figure 1 Analyses of oil compositions and oil synthesis-related genes in the seeds of Camellia lanceoleosa(Adopted from Gong et al., 2022) Image caption: (a) Oil body showing red autofluorescence under a 512-nm laser with a confocal microscope. (b) Fatty-acid component analysis with gas chromatography-mass spectrometry. The number next to the peak denotes the retention time. (c) Fatty-acid compositions in mature seeds. %, percentage of all fatty acids detected. Expression patterns of genes encoding homogeneous and heteromeric ACCase, stearoyl-ACP desaturase, omega-6 fatty acid desaturase and omega-3 fatty acid desaturase, diacylglycerol O-acyltransferase and glycerol-3-phosphate dehydrogenase in different tissues and developmental seeds of C. lanceoleosa (d) and Camellia sinensis (e). Three independent measurements were used to calculate the mean and standard deviation values (Adopted from Gong et al., 2022) 3.3 Publicly available databases and resources Several publicly available databases and resources have been developed to facilitate the conservation and utilization of Camellia germplasm. For example, a web-accessible database has been created for efficient retrieval of Camellia transcriptomes, which includes data from the pan-transcriptome of 116 Camellia plants (Wu et al., 2022). Additionally, unigene derived microsatellite markers (UGMS) identified from publicly available sequence databases have been developed for genetic analysis, gene mapping, and marker-assisted breeding in tea. These resources, along with the extensive transcriptome datasets and genome assemblies, provide a robust platform for genomic, transcriptomic, and functional genomic studies in Camellia species, thereby accelerating breeding strategies and improving tea plant varieties (Wei et al., 2018).
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