The Transcriptome and Terpene Profile of Eucalyptus grandis Reveals Mechanisms of Defense Against the Insect Pest, Leptocybe invasa.

Plants have evolved complex defenses that allow them to protect themselves against pests and pathogens. However, there is relatively little information regarding the Eucalyptus defensome. Leptocybe invasa is one of the most damaging pests in global Eucalyptus forestry, and essentially nothing is known regarding the molecular mechanisms governing the interaction between the pest and host. The aim of the study was to investigate changes in the transcriptional landscape and terpene profile of a resistant and susceptible Eucalyptus genotype in an effort to improve our understanding of this interaction. We used RNA-seqencing to investigate transcriptional changes following L. invasa oviposition. Expression levels were validated using real-time quantitative PCR. Terpene profiles were investigated using gas chromatography coupled to mass spectometry on uninfested and oviposited leaves. We found 698 and 1,115 significantly differentially expressed genes from the resistant and susceptible interactions, respectively. Gene Ontology enrichment and Mapman analyses identified putative defense mechanisms including cell wall reinforcement, protease inhibitors, cell cycle suppression and regulatory hormone signaling pathways. There were significant differences in the mono- and sesquiterpene profiles between genotypes and between control and infested material. A model of the interaction between Eucalyptus and L. invasa was proposed from the transcriptomic and chemical data.

Classification of Cannabis Strains Based on their Chemical Fingerprint-A Broad Analysis of Chemovars in the German Market.

Introduction: Cannabis cultivars were usually categorized based on their genetic profile as sativa, indica, or hybrid types. However, these three criteria do not allow sufficient differentiation between the numerous varieties of cannabis strains. Furthermore, this classification is based on morphological and bio-geographical properties of the plants and does not represent the chemical composition of different cultivars. The concentration of cannabinoids and terpenes are crucial for the pharmacological effect, not only because of the known entourage effect, and therefore needs to be considered by categorization. Materials and Methods: A total of 140 medicinal cannabis flowers available on the German market were analyzed regarding their individual terpene profile using GC-MS analysis. Statistical evaluation was performed to investigate correlations and data relations as well as for clustering. Results: Multivariate analysis showed correlations between individual terpenes. However, there was no statistical correlation between terpene profiles and their respective genetic profile. Terpene profiles of sativa, indica, and hybrid strains are quite heterogenous and clearly showed that there is no relation between terpenes and the estimated pharmacological effect. As a result, we suggest a new classification system based on individual terpene profiles to faster a comprehensive understanding of the expected medical effect. Discussion: Considering main terpenes, we established a concept of six clusters with various terpene profiles being attributed to different medicinal applications. We excluded tetrahydrocannabinol (THC) and cannabidiol (CBD) content from clustering as most of the strains were THC dominant and therefore distort the results. Our pattern of strains with similar terpene profiles might refine the existing classes of chemotypes with different THC:CBD content. Conclusion: The categorization of cannabis strains based on their terpene profiles allows a clearer, finer and, above all, more meaningful classification than the existing sativa/indica classification. Due to the entourage effect and the interactions between cannabinoids and terpenes, this group of substances is also given the necessary consideration when selecting the right medicine for the individual. Within the next steps, further studies are needed with the aim of mapping clinical validated effects to our chemovars. If it is possible to correlate therapy of symptoms to specific chemical profiles personalized cannabinoid therapy will be possible.