A versatile, rapid Agrobacterium-mediated transient expression system for functional genomics studies in cannabis seedling.

MAIN CONCLUSION: We have developed and optimized a rapid, versatile Agrobacterium-mediated transient expression system for cannabis seedlings that can be used in functional genomics studies of both hemp-type and drug-type cannabis. Cannabis (Cannabis sativa L.) holds great promise in the medical and food industries due to its diverse chemical composition, including specialized cannabinoids. However, the study of key genes involved in various biological processes, including secondary metabolite biosynthesis, has been hampered by the lack of efficient in vivo functional analysis methods. Here, we present a novel, short-cycle, high-efficiency transformation method for cannabis seedlings using Agrobacterium tumefaciens. We used the RUBY reporter system to monitor transformation results without the need for chemical treatments or specialized equipment. Four strains of A. tumefaciens (GV3101, EHA105, LBA4404, and AGL1) were evaluated for transformation efficiency, with LBA4404 and AGL1 showing superior performance. The versatility of the system was further demonstrated by successful transformation with GFP and GUS reporter genes. In addition, syringe infiltration was explored as an alternative to vacuum infiltration, offering simplicity and efficiency for high-throughput applications. Our method allows rapid and efficient in vivo transformation of cannabis seedlings, facilitating large-scale protein expression and high-throughput characterization studies.

Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.

Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the developmental changes and the underlying molecular processes involved in cannabis GT development is limited. In this study, we developed Cannabis Glandular Trichome Detection Model (CGTDM), a deep learning-based model capable of differentiating and quantifying three types of cannabis GTs with a high degree of efficiency and accuracy. By profiling at eight different time points, we captured dynamic changes in gene expression, phenotypes, and metabolic processes associated with GT development. By integrating weighted gene co-expression network analysis with CGTDM measurements, we established correlations between phenotypic variations in GT traits and the global transcriptome profiles across the developmental gradient. Notably, we identified a module containing methyl jasmonate (MeJA)-responsive genes that significantly correlated with stalked GT density and cannabinoid content during development, suggesting the existence of a MeJA-mediated GT formation pathway. Our findings were further supported by the successful promotion of GT development in cannabis through exogenous MeJA treatment. Importantly, we have identified CsMYC4 as a key transcription factor that positively regulates GT formation via MeJA signaling in cannabis. These findings provide novel tools for GT detection and counting, as well as valuable information for understanding the molecular regulatory mechanism of GT formation, which has the potential to facilitate the molecular breeding, targeted engineering, informed harvest timing, and manipulation of cannabinoid production.