The characterization of key physiological traits of medicinal cannabis (Cannabis sativa L.) as a tool for precision breeding

Intra-leaf modeling of Cannabis leaflet shape produces leaf models that predict genetic and developmental identities

The iconic, palmately compound leaves of Cannabis have attracted significant attention in the past. However, investigations into the genetic basis of leaf shape or its connections to phytochemical composition have yielded inconclusive results. This is partly due to prominent changes in leaflet number within a single plant during development, which has so far prevented the proper use of common morphometric techniques. Here, we present a new method that overcomes the challenge of nonhomologous landmarks in palmate, pinnate, and lobed leaves, using Cannabis as an example. We model corresponding pseudo-landmarks for each leaflet as angle-radius coordinates and model them as a function of leaflet to create continuous polynomial models, bypassing the problems associated with variable number of leaflets between leaves. We analyze 341 leaves from 24 individuals from nine Cannabis accessions. Using 3591 pseudo-landmarks in modeled leaves, we accurately predict accession identity, leaflet number, and relative node number. Intra-leaf modeling offers a rapid, cost-effective means of identifying Cannabis accessions, making it a valuable tool for future taxonomic studies, cultivar recognition, and possibly chemical content analysis and sex identification, in addition to permitting the morphometric analysis of leaves in any species with variable numbers of leaflets or lobes.

Characterization of indigenous populations of cannabis in Iran: a morphological and phenological study

BACKGROUND

Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars.

RESULTS

Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g- 1.day- 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants.

CONCLUSIONS

Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes.

Plant Growth-Promoting Rhizobacteria (PGPR) with Microbial Growth Broth Improve Biomass and Secondary Metabolite Accumulation of Cannabis sativa L

Plant growth-promoting rhizobacteria (PGPR) are a sustainable crop production input; some show positive effects under laboratory conditions but poorly colonize host field-grown plants. Inoculating with PGPR in microbial growth medium (e.g., King's B) could overcome this. We evaluated cannabis plant (cv. CBD Kush) growth promotion by inoculating three PGPR (Bacillus sp., Mucilaginibacter sp., and Pseudomonas sp.) in King's B at vegetative and flower stages. At the vegetative stage, Mucilaginibacter sp. inoculation increased flower dry weight (24%), total CBD (11.1%), and THC (11.6%); Pseudomonas sp. increased stem (28%) dry matter, total CBD (7.2%), and THC (5.9%); and Bacillus sp. increased total THC by 4.8%. Inoculation with Mucilaginibacter sp. and Pseudomonas sp. at the flowering stage led to 23 and 18% increases in total terpene accumulation, respectively. Overall, vegetative inoculation with PGPR enhanced cannabis yield attributes and chemical profiles. Further research into PGPR inoculation onto cannabis and the subsequent level of colonization could provide key insights regarding PGPR-host interactions.

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Moving Away from 12:12; the Effect of Different Photoperiods on Biomass Yield and Cannabinoids in Medicinal Cannabis

The standard practice to initiate flowering in medicinal cannabis involves reducing the photoperiod from a long-day period to an equal duration cycle of 12 h light (12L)/12 h dark (12D). This method reflects the short-day flowering dependence of many cannabis varieties but may not be optimal for all. We sought to identify the effect of nine different flowering photoperiod treatments on the biomass yield and cannabinoid concentration of three medicinal cannabis varieties. The first, "Cannatonic", was a high cannabidiol (CBD)-accumulating line, whereas the other two, "Northern Lights" and "Hindu Kush", were high Δ9-tetrahydrocannabinol (THC) accumulators. The nine treatments tested, following 18 days under 18 h light/6 h dark following cloning and propagation included a standard 12L:12D period, a shortened period of 10L:14D, and a lengthened period of 14L:10D. The other six treatments started in one of the aforementioned and then 28 days later (mid-way through flowering) were switched to one of the other treatments, thus causing either an increase of 2 or 4 h, or a decrease of 2 or 4 h. Measured parameters included the timing of reproductive development; the dry weight flower yield; and the % dry weight of the main target cannabinoids, CBD and THC, from which the total g cannabinoid per plant was calculated. Flower biomass yields were highest for all lines when treatments started with 14L:10D; however, in the two THC lines, a static 14L:10D photoperiod caused a significant decline in THC concentration. Conversely, in Cannatonic, all treatments starting with 14L:10D led to a significant increase in the CBD concentration, which led to a 50-100% increase in total CBD yield. The results show that the assumption that a 12L:12D photoperiod is optimal for all lines is incorrect as, in some lines, yields can be greatly increased by a lengthened light period during flowering.

The Cannabis Plant as a Complex System: Interrelationships between Cannabinoid Compositions, Morphological, Physiological and Phenological Traits

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant's cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids' qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ⁹-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ⁹-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances.

Natural products of medicinal plants: biosynthesis and bioengineering in post-genomic era

Globally, medicinal plant natural products (PNPs) are a major source of substances used in traditional and modern medicine. As we human race face the tremendous public health challenge posed by emerging infectious diseases, antibiotic resistance and surging drug prices etc., harnessing the healing power of medicinal plants gifted from mother nature is more urgent than ever in helping us survive future challenge in a sustainable way. PNP research efforts in the pre-genomic era focus on discovering bioactive molecules with pharmaceutical activities, and identifying individual genes responsible for biosynthesis. Critically, systemic biological, multi- and inter-disciplinary approaches integrating and interrogating all accessible data from genomics, metabolomics, structural biology, and chemical informatics are necessary to accelerate the full characterization of biosynthetic and regulatory circuitry for producing PNPs in medicinal plants. In this review, we attempt to provide a brief update on the current research of PNPs in medicinal plants by focusing on how different state-of-the-art biotechnologies facilitate their discovery, the molecular basis of their biosynthesis, as well as synthetic biology. Finally, we humbly provide a foresight of the research trend for understanding the biology of medicinal plants in the coming decades.

Semi-quantitative analysis of cannabinoids in hemp (Cannabis sativa L.) using gas chromatography coupled to mass spectrometry

Background

Hemp (Cannabis sativa L.) is a producer of cannabinoids. These organic compounds are of increasing interest due to their potential applications in the medicinal field. Advances in analytical methods of identifying and quantifying these molecules are needed.

Method

This study describes a new method of cannabinoid separation from plant material using gas chromatography-mass spectrometry (GC-MS) as the analytical tool to detect low abundance cannabinoids that will likely have implications for future therapeutical treatments. A novel approach was adopted to separate trichomes from plant material to analyse cannabinoids of low abundance not observed in raw plant extract. Required plant sample used for analysis was greatly reduced compared to other methods. Derivatisation method was simplified and deconvolution software was utilised to recognise unknown cannabinoid compounds of low abundance.

Results

The method produces well-separated spectra and allows the detection of major and minor cannabinoids. Ten cannabinoids that had available standards could be identified and quantified and numerous unidentified cannabinoids or pathway intermediates based on GC-MS spectra similarities could be extracted and analysed simultaneously with this method.

Conclusions

This is a rapid novel extraction and analytical method from plant material that can identify major and minor cannabinoids using a simple technique. The method will be of use to future researchers seeking to study the multitude of cannabinoids whose values are currently not understood.

Phenotype correlation analysis and excellent germplasm screening of herb Bletilla Rchb. f. based on comprehensive evaluation from thirty-three geographic populations

BACKGROUND

The Bletilla genus of Orchidaceae includes plants with great economic value, among which B. striata is the main traditional medicinal plant, and its pseudobulb, known as BaiJi, was first recorded in Shennong's Classic of Materia Medica. However, there has been little systemic evaluation of the germplasm quality of Bletilla plants in China. In order to comprehensive evaluate the Bletilla resources in China and screen out the candidate phenotypic traits determining yield and/or quality of Bletilla, the variation of phenotypic indicators (pseudobulb, leaf, stem, inflorescence, flower) and active ingredients contents (polysaccharide, total phenolics and militarine) in different populations of B. striata and B. ochracea were investigated through 4 years' common-garden experiment.

RESULTS

There were abundant phenotypic variations and significant differences among different populations in the morphological phenotypes, pseudobulb weight and main active ingredient contents. AHBZ, HBLT and HBSN populations showed good prospects for industrial development, presenting higher quality in terms of yield and main active ingredient content. Pseudobulb yield, polysaccharide and total phenol content are positively correlated with phenotypic traits. Militarine content is negatively correlated with almost all indexes. Plant height, leaf width and stem diameter may be important indicators of potential excellent germplasms.

CONCLUSIONS

Bletilla is not strictly geoauthentic medicinal plants. B. ochracea could be accepted as an alternative resource to B. striata. The best harvest period of Bletilla is the third year after cultivation. Plant height, leaf width and stem diameter may be important indicators of potential excellent germplasms. These results provide important information required for the efficient screening and utilization of Bletilla germplasm resources.

Impact of Three Different Light Spectra on the Yield, Morphology and Growth Trajectory of Three Different Cannabis sativa L. Strains

Cannabis is one of the oldest cultivated plants, but plant breeding and cultivation are restricted by country specific regulations. Plant growth, morphology and metabolism can be manipulated by changing light quality and intensity. Three morphologically different strains were grown under three different light spectra with three real light repetitions. Light dispersion was included into the statistical evaluation. The light spectra considered had an influence on the morphology of the plant, especially the height. Here, the shade avoidance induced by the lower R:FR ratio under the ceramic metal halide lamp (CHD) was of particular interest. The sugar leaves seemed to be of elementary importance in the last growth phase for yield composition. Furthermore, the last four weeks of flowering were crucial to influence the yield composition of Cannabis sativa L. through light spectra. The dry flower yield was significantly higher under both LED treatments compared to the conventional CHD light source. Our results indicate that the plant morphology can be artificially manipulated by the choice of light treatment to create shorter plants with more lateral branches which seem to be beneficial for yield development. Furthermore, the choice of cultivar has to be taken into account when interpreting results of light studies, as Cannabis sativa L. subspecies and thus bred strains highly differ in their phenotypic characteristics.