Beyond cannabinoids: Application of NMR-based metabolomics for the assessment of Cannabis sativa L. crop health

Abstract

1. Introduction

Cannabis (Cannabis sativa L.) is a widely distributed plant that has been a source of fiber, food, oil, and medicines for millennia (Clarke and Merlin, 2016). The first reports regarding its medicinal use date back to the third millennium BC in ancient China (Wills, 1998). Since then, several cultures have employed cannabis preparations to treat a variety of ailments (Russo, 2014). Since our understanding of its therapeutic potential is continuously improving, the interest in cannabis as a source of compounds for use in medicinal preparations has increased notably (Procaccia et al., 2022). Furthermore, the non-pharmacological uses of the plant and its derivatives, particularly as a source of fibers and as a recreational drug, are well known.

C. sativa produces an important number of secondary metabolites, and more than 500 compounds have been identified in cannabis so far (ElSohly and Gul, 2014). In particular, cannabinoids, terpenoids, and flavonoids stand out (ElSohly and Slade, 2005). Of the three compound classes, the former are of great relevance given their biological activity and the fact that they are almost exclusively found in this plant (Hanuš et al., 2016; Gülck and Møller, 2020). Most of the known pharmacological properties are associated to these naturally-occurring compounds, particularly Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD). These compounds are present mostly as carboxylic acids in the plant, namely Δ⁹-tetrahydrocannabinolic and cannabidiolic acids (THCA and CBDA), and are transformed to the neutral molecules through non-enzymatic decarboxylation (Flores-Sanchez and Verpoorte, 2008). However, the biological effects of C. sativa do not depend only on the levels of THC and CBD (Procaccia et al., 2022). Indeed, nearly 150 phytocannabinoids which can be classified in different structural subclasses have been isolated from this plant (Hanuš et al., 2016), and several of these compounds, sometimes referred to as minor cannabinoids, have pharmacological potential (Franco et al., 2020). Furthermore, a number of studies suggest that the interaction or synergy between these cannabinoids and other secondary metabolites leads to more pronounced effects than those produced by the isolated compounds (Russo, 2011; Blasco-Benito et al., 2018; Russo, 2019; Maayah et al., 2020; Goerl et al., 2021). In addition, the botanical diversity of C. sativa has resulted in longstanding discussions regarding its taxonomical characterization. It is now generally accepted that the plant is a single species that can be classified in different subspecies and varieties (Small, 2015; McPartland, 2018). Independently of the names and botanical traits used to refer to it, the chemical composition is what will determine the pharmacological usefulness of the plant. Therefore, several authors stress that the categorization of C. sativa varieties should be done according to its chemical profile (Russo, 2019), and refer to them as chemical varieties or chemovars (Lewis et al., 2018).

In addition to secondary metabolite content, the quality of a medicinal plant is also given by the absence of contaminants and adulterants. Natural contaminants are usually introduced during cultivation and storage, and consist of degradation products, microbial contamination, and heavy metals (American Herbal Pharmacopoeia, 2014). In particular, fungi can colonize different organs of the plant, causing a reduction in the quality of the products (Punja et al., 2019). Furthermore, the consumption of a contaminated product can cause harmful effects on the health of consumers (Montoya et al., 2020). Microbial infections can also lead to biotic stress, which can alter the secondary metabolite composition (Gorelick and Bernstein, 2017).

While the determination of the main cannabinoid content is a required assay for cannabis products that rely on the biological activity of the plant extract, comprehensive knowledge of the profile of other secondary metabolites in these materials is also relevant (Jin et al., 2021). Thus, establishing the metabolic fingerprints can provide important information to assure the composition and quality of a given C. sativa chemovar (Puri et al., 2021). Variations in this profile can indicate that the material is not appropriate for its intended uses. In particular, the discrimination between chemovars can be conveniently carried out through NMR-based metabolomic analysis (Choi et al., 2004a). Indeed, a recent review concludes that metabolomics represents an ideal bioanalytical tool that could greatly assist and accelerate cannabis research and development, even coining the term “cannabinomics”(Aliferis and Bernard-Perron, 2020).

In the present report we carry out an exhaustive characterization of a high-THCA and a high-CBDA C. sativa chemovars, both with medicinal potential, through the determination of metabolite profiles complemented with basic information derived from cannabinoid composition. Briefly, our results not only allowed us to clearly distinguish between the two plant varieties, but also to identify a crop with a microbial infection that, interestingly, does not alter the main cannabinoid profile and yield in a relevant way. Furthermore, through metabolomic analysis it was possible to identify variations in specific metabolites that could shed light on infection mechanisms.

2. Materials and methods

3. Results

Tables 1, 2 show the cannabinoid composition of chemovars A and B. As expected, the two are notably different, and characterized, respectively, by their particularly high content of THCA and CBDA. Moreover, the acidic forms of other cannabinoids are also predominant, and CBN was only detected in one sample and below the quantification limits. This suggests that little degradation occurred during sample handling and analysis. For chemovar A, the total cannabinoid content ranged from 15.14 ± 0.50 to 16.70 ± 1.10%, with a mean of 15.50 ± 1.10% (n = 6, two samples per crop, three crops). Similarly, the cannabinoid content in chemovar B crops ranged from 16.50 ± 0.43 to 18.36 ± 0.08% with a mean of 17.60 ± 0.80% (n = 6, two samples per crop, three crops). These results agree with literature reports, which indicate average cannabinoid contents in inflorescences ranging from 5 to 25% (Jin et al., 2021). Inspection of the results also shows that relative cannabinoid composition and total cannabinoid yield is stable through the crops in both chemovars.

Following the determination of the basic cannabinoid composition, the metabolic profiles for both C. sativa varieties were investigated. For this purpose, 20 samples of each chemovar from the three harvests considered were collected, resulting in a total of 60 organic and 60 aqueous extract samples. PCA score plots obtained from the ¹H NMR data recorded for the two sets are shown in Figure 1. It is clear that both chemovars can be easily differentiated on the basis of NMR data from their organic or aqueous extracts. Indeed, the first two PCs account for 73.7% of the variance in the organic set, with samples from chemovar A forming a cluster showing positive PC1 scores, whereas chemovar B samples have negative scores on this PC. Similarly, only two PCs were sufficient to account for nearly 90% of the variance in the aqueous data set.

Cursory inspection of both PCA plots indicates that six of the aqueous samples corresponding to the third crop of chemovar A deviate from the others, suggesting errors during the preparation of these samples. On the other hand, all 20 organic and aqueous extracts from the third crop of chemovar B deviate considerably from the rest. As discussed below, this is likely associated with fungal infections affecting plants from this particular crop. Therefore, the PCA score plots were recomputed without the outliers from both chemovars. As shown in Figure 2, ¹H NMR profiles from organic and aqueous extracts can still differentiate the two chemovars clearly.

In order to identify the metabolites that differentiate the two chemovars, OPLS-DA models from the depurated organic and aqueous extract ¹H NMR datasets were generated. Inspection of the OPLS-DA loading factor plot obtained from organic extracts indicates that signals at 1.44, 1.25, and 1.11 ppm, corresponding to THCA, correlate to chemovar A, while signals for CBDA at 1.80 and 1.72 ppm corroborate that the levels of this cannabinoid are higher in chemovar B (Figure 3) (Choi et al., 2004b). These results are not surprising and are consistent with those obtained by the HPLC analyses presented in Tables 1, 2.

When the loading factor plot of the OLPS-DA between aqueous extracts of the two chemovars was analyzed (Figure 4), positive signals at 4.10, 4.03-3.97, 3.88, 3.82-3.77, 3.70-3.63, and 3.59-3.52 ppm, and at 3.99, 2.93, and 2.84 ppm, which can be respectively assigned to fructose and aspartate by comparison to literature data (Wishart et al., 2009; Barclay et al., 2012; de Falco et al., 2016), show that the levels of these two metabolites are higher in chemovar B plants. In addition, a positive signal at 3.23 ppm with an HSQC correlation to a ¹³C resonance at 53.4 ppm likely corresponds to the N-methyl groups of betaine (de Falco et al., 2016; Wishart et al., 2022), indicating that the levels of this trimethylammonium-bearing metabolite are also higher in the high-CBDA chemovar specimens (Figure S3). This initial assignment was confirmed by spiking one of the aqueous extract samples with a betaine standard solution (see Figure S4). On the other hand, negative peaks at 4.24, 4.04, 3.73, 3.59, 3.57, 3.44, and 3.38 ppm match nicely with reference data for the carbasugar quebrachitol (De Almeida et al., 2012). This reveals that the concentration of this cyclitol, whose identity was further established by means of STOCSY, 1D-TOCSY, 1D-NOESY, and heteronuclear correlation NMR experiments (see Figures S5–S7; Table S1), is higher in chemovar A plants. Moreover, a negative singlet at 3.18 ppm correlating to a ¹³C resonance at 53.9 ppm in the HSQC spectrum corresponds to choline (de Falco et al., 2016; Wishart et al., 2022), suggesting that the levels of this quaternary aminoalcohol are higher in the high-THCA C. sativa variety (Figure S8). As was the case for betaine, this assignment was confirmed unequivocally by spiking experiments (Figure S4).

Plants from the third crop of chemovar B showed signs of powdery mildew infection. The morphological characteristics of this fungal leaf growth and its asexual microscopic structures allowed us to classify it as a member of the Golovinomyces genus (Figures S11, S12). Since this infection was also clearly evidenced in the initial clustering analyses depicted in Figure 1, we decided to carry out a more exhaustive comparison between healthy and infected plants within this chemovar using OPLS-DA. As shown in Figure 5, the model obtained with ¹H NMR data from organic extracts easily differentiates inflorescences of the first two crops (B1+B2) from those of the third (B3). The corresponding loading factor plot identifies CBDA as one of the discriminating metabolites in this case, and, in agreement with HPLC findings presented in Table 2, indicates that higher concentrations of this cannabinoid are present in healthy plants of the high-CBDA chemovar. On the other hand, higher levels of waxes, with signals at 2.42, 1.70, 1.28-1.25, and 0.92 ppm (Stoianova-Ivanova et al., 1974), are found in infected plant extracts.

The OPLS-DA model comparing aqueous extracts from the B1+B2 harvest versus those from B3 can also classify healthy and infected plants correctly (Figure 6). As observed in the loading factor plot, aspartate, choline, and fructose are in this case associated with healthy chemovar B specimens, while the levels of betaine are higher in infected plants.

4. Discussion

As stated in the introduction, our initial goal was to perform an exhaustive chemical characterization of a high-THCA and a high-CBDA C. sativa chemovars with medicinal potential through the determination of their metabolic profiles. Our results revealed a number of differences between the two varieties beyond their cannabinoid composition. This is an important finding since more than five hundred different compounds have been reported in the plant, some with potential therapeutic qualities, and combinations of the various secondary metabolites could determine both the final medicinal response as well as any adverse effects (Lowe et al., 2021; Procaccia et al., 2022). In fact, and despite the results from NMR-based metabolomic analysis of organic extracts presented in Figures 2, 3 parallel those obtained through HPLC (Tables 1, 2), the study of aqueous fractions identified aspartate, betaine, choline, fructose, and quebrachitol as metabolites that can further discriminate between the two chemovars. The differences in sugar and aminoacid contents, which have been described by Choi and coworkers in their seminal report on the application of metabolomic profiling to the characterization of cannabis cultivars (2004a), could be employed to better fingerprint cannabis varieties and products intended for use as botanical drugs. This is also the case for quebrachitol, a long-known component of C. sativa (Adams et al., 1940; Groce and Jones, 1973). Indeed, variations in the concentration of this cyclitol have also been employed to address the phenotypical plasticity in other plants (Kortesniemi et al., 2017), making it a promising phytomarker for cultivar differentiation. Given the correlation between betaine levels and abiotic stress (Rhodes and Hanson, 1993), the identification of higher concentrations of this metabolite in high-CBDA chemovars suggest better adaptation to drought, high salinity, and low temperature conditions. In principle, this information would be instrumental to breeders in the development of plant varieties agronomically adapted to specific environmental conditions.

In addition to differences between varieties, analysis of the data from different harvests also allowed us to identify changes in the metabolome caused by fungal infection. As summarized in Figures 5, 6, higher levels of CBDA, fructose, aspartate, and choline were observed in healthy plants from chemovar B, while increased levels of waxes and betaine were found in infected specimens. It is well known that biotic and abiotic stresses, and in particular bacterial and fungal infections, can lead to variations in the cannabinoid profile in C. sativa (Gorelick and Bernstein, 2017). While these variations were not evident when comparing the cannabinoid compositions of the three crops using data from standard HPLC methods, multivariate analysis of NMR data was highly sensitive and allowed to readily detect them. Indeed, CBDA was identified as one of the metabolites that can aid in the classification of healthy and diseased plants. While HPLC revealed a slightly lower concentration for this metabolite in the third crop relative to the first two (13.23% versus 14.30 and 14.38%), the variation is well-within quality specifications proposed for botanical drugs (Sarma et al., 2020). Furthermore, the full cannabinoidic profile obtained by HPLC was the same for the three crops. In agreement with their roles in stress response, the concentration of waxes and betaine was also higher in plants with signs of infection. The former compounds constitute one of the first lines of defense against microbes (Moyna and Heinzen, 2001; Arya et al., 2021). While larger alterations in wax composition could be expected in leaves, variations in its concentration in inflorescences are also likely upon fungal infection (Inada and Savory, 2011). Similarly, and taking into account its role as an osmoprotectant that counteracts the effects of reactive oxygen species and promotes membrane stabilization (Rhodes and Hanson, 1993), variations in betaine levels are consistent with the necrotic lesions observed on plant tissue affected by powdery mildew (Punja et al., 2019). As discussed above, variations in betaine concentration in plants are normally associated with abiotic stress. However, there is evidence that fungal infections can also lead to increases in the levels of this quaternary ammonium alcohol in other crops (Murray and Ayres, 1986; Aslanpour et al., 2016).

To summarize, our studies contribute to the holistic chemical characterization of C. sativa varieties with potential medicinal applications. Given the variability in the chemical composition of these plants and their extracts, this information is critical to assure the quality of botanical drugs and related products derived from them. In addition, the results presented above further showcase the suitability of metabolomic profiling as a tool for the rational classification of plant materials.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

SF: methodology, data curation, formal analysis, investigation, and writing–original draft. RC: data curation and formal analysis. AL-R: data curation, formal analysis, and investigation. PR: data curation, formal analysis, and investigation. IC: project administration, funding acquisition, and writing–review and editing. CG-C: conceptualization, methodology, project administration, funding acquisition, and writing–review and editing. GM: conceptualization, methodology, project administration, and writing-review and editing. All authors contributed to the article and approved the submitted version.

Acknowledgments

Funding Statement

This work was jointly funded by Agencia Nacional de Investigación e Innovación (ANII) and Khiron Life Sciences Uruguay S.A. (award ALI_1_2018_1_147904). Additional financial support was received from the Programa para el Desarrollo de las Ciencias Básicas (PEDECIBA).

References