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

. 2023 Mar 22;14:1025932.


doi: 10.3389/fpls.2023.1025932.


eCollection 2023.

Affiliations

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Santiago Fernández et al.


Front Plant Sci.


.

Abstract

While Cannabis sativa L. varieties have been traditionally characterized by their major cannabinoid profile, it is now well established that other plant metabolites can also have physiological effects, including minor cannabinoids, terpenes, and flavonoids. Given the multiple applications of cannabis in the medical field, it is therefore critical to characterize it according to its chemical composition (i.e., its metabolome) and not only its botanical traits. With this in mind, the cannabinoid and metabolomic profiles from inflorescences of two C. sativa varieties with either high Δ9-tetrahydrocannabinolic acid (THCA) or high cannabidiolic acid (CBDA) contents harvested at different times were studied. According to results from HPLC and NMR-based untargeted metabolomic analyses of organic and aqueous plant material extracts, we show that in addition to expected variations according to cannabinoid profiles, it is possible to distinguish between harvests of the same variety. In particular, it was possible to correlate variations in the metabolome with presence of powdery mildew, leading to the identification of molecular markers associated with this fungal infection in C. sativa.


Keywords:

Cannabis sativa; NMR-based metabolomics; cannabinoids; chemovar; powdery mildew.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Khiron Life Sciences Uruguay S.A. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

Figures


Figure 1



Figure 1

PCA score plots of organic (A) and aqueous (B) extracts from chemovars A and B colored according to harvest.


Figure 2



Figure 2

PCA score plots of organic (A) and aqueous (B) extracts from chemovars A and B after removal of outliers.


Figure 3



Figure 3

Score and loading factor plots obtained from the OPLS-DA between organic extracts of chemovars A and B. The metabolites that differentiate the two groups are annotated in the loading factor plots. The R2Y and Q2Y coefficients for the model were 0.99 and 0.99, respectively, and the ROC curve had an AUC value of 1.00 (see
Figures S1
,
S2
).


Figure 4



Figure 4

Score and loading factor plots obtained from the OPLS-DA between aqueous extracts of chemovars A and B. The metabolites that differentiate the two groups are annotated in the loading factor plots. The R2Y and Q2Y coefficients for the model were 0.91 and 0.83, respectively, and the ROC curve had an AUC value of 0.98 (see
Figures S9
,
S10
).


Figure 5



Figure 5

Score and loading factor plots obtained from the OPLS-DA between organic extracts of healthy and infected crops of chemovar B. The metabolites that differentiate the two groups are annotated in the loading factor plots. The R2Y and Q2Y coefficients for the model were 0.98 and 0.91, respectively, and the ROC curve had an AUC value of 1.00 (see
Figures S13
,
S14
).


Figure 6



Figure 6

Score and loading factor plots obtained from the OPLS-DA between aqueous extracts of healthy and infected crops of chemovar B. The metabolites that differentiate the two groups are annotated in the loading factor plots. The R2Y and Q2Y coefficients for the model were 0.99 and 0.98, respectively, and the ROC curve had an AUC value of 1.00 (see
Figures S15
,
S16
).

References

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Grant support

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).

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