Elemental Analysis for the Optimization of Plant Secondary Metabolites Using Cannabis sativa as a Model Organism

doi:10.21203/rs.3.rs-4921004/v1

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Elemental Analysis for the Optimization of Plant Secondary Metabolites Using Cannabis sativa as a Model Organism

https://doi.org/10.21203/rs.3.rs-4921004/v1

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When cultivating medicinally valuable plants, it is often beneficial to assess production of secondary metabolites as these can act as markers for desired traits. Cannabis is one such medicinally valuable plant that has gained much attention recently due to its growing availability. Chemometric methods can be applied to understand the relationships governing the production of these compounds and optimize cultivation toward certain ends. In the current study, thirteen elements were quantitatively measured in the leaves of cannabis plants using inductively coupled plasma mass spectroscopy and an Elementar vario macro cube. Correlation analysis, principal components analysis, and K-means clustering were utilized to describe and elucidate trends in the dataset. Moderately positive, monotonic correlations were found between magnesium, boron, and calcium, along with nitrogen, sulfur, and copper. PCA was used to corroborate these relationships. Clustering analysis was able to identify three distinct groups to which strains could be mapped with a relatively high degree of resolution when compared to cultivator identifiers. These findings suggest similar methods of introduction and elemental incorporation into the strains of these distinct groups. The methods utilized in the current study serve as the basis for the future development of methods that may be utilized in the optimization of secondary metabolite production.

Chemometrics

Secondary Metabolites

Elemental Analysis

PCA

K-means

Cannabis

Plant derived secondary metabolites are metabolic intermediates that do not factor into regular growth and development of the plant, but which play a crucial role in allowing an organism to adapt to its environment (1). This includes responding to both biotic and abiotic stress, as these compounds have been shown to possess antibiotic, antifungal, antiviral, and anti-UV properties in a wide variety of organisms (1, 2). For centuries, plant-based medicine has relied on secondary metabolites to elicit its physiological effects (1). Such notable examples include morphine, an alkaloid derived from the poppy plant, and acetylsalicylate, whose precursor can be found in the bark of the willow tree (1, 3). In addition to their considerable medical value, the application of secondary metabolites stretches beyond the pharmaceutical industry, as similar compounds are also utilized in the production of cosmetics, nutritional supplements, and flavorings (2). For many commercial applications, these compounds continue to be produced and isolated from cultivated organisms, meaning the quality of the final product is heavily dependent on the cultivation practices utilized during plant growth. Due to the medical and commercial applications of secondary metabolites, research addressing the mechanisms surrounding their production in vivo continues to receive widespread attention.

Cannabis sativa is one such plant whose value stems from its secondary metabolites. Its well documented pharmacological effects can be attributed to over 100 identified phytocannabinoids, a naturally occurring class of secondary metabolites (4, 5). Of these cannabinoids, the non-psychoactive cannabidiol (CBD) and the psychoactive tetrahydrocannabinol (Δ⁹ THC) have received the most attention (6). Though these compounds are often cited to explain cannabis’ physiological effects, synthetically derived versions of these compounds have often displayed decreased efficacy compared to treatment with plant material itself (7, 8). This suggests the presence of a physiological effect brought about by a combination of cannabis metabolites working in tandem, rather than any single compound producing a single effect (8). This phenomenon has been termed the entourage effect due to the interaction of these various compounds and underscores the importance of understanding how these secondary metabolites are produced both within the plant and in relation to one another.

Chemometric methods have long been directed toward medicinally valuable plants with the aim of understanding the mechanisms which govern specific metabolic pathways (9). These methods allow for the application of statistical techniques to analyze and interpret complex, multivariate datasets (10). Historically, these techniques have been utilized to classify crop strains and improve plant quality by guiding cultivation (11). Due to cannabis’ long history of criminalization, existing chemometric analyses on the plant are limited. However, with modern cultivators’ strong focus on controlling the production of cannabis’ medicinally valuable cannabinoids, it is an ideal subject for such analysis.

This study aimed to analyze the relationships among element concentrations in cannabis leaf tissue in the interest of developing a process by which distinct cannabis strains can be categorized utilizing chemical data. Carrying out this process with elemental data serves as a model for applying the same methodology to specific secondary metabolites, allowing for the identification of the underlying chemical relationships which influence plant composition. Subsequent optimization of the cultivation process can be achieved toward specific ends, including the amplification or suppression of secondary metabolites of interest. Among similar works, no study could be found that possessed a dataset of this size. In conjunction with quantitative data regarding secondary metabolites, these methods may be utilized to optimize the cultivation of cannabis to produce specific analytes. It is this study’s aim that, in outlining a methodology for cannabis, it may be applied to other medicinally valuable plants toward the end of optimizing cultivation to produce specific secondary metabolites.

1317 samples of cannabis leaf tissue were collected from plants at various points in the flowering phase of growth. The cannabis samples were donated by cannabis producers throughout Michigan and tested by Cambium Analytica, an independently contracted laboratory certified by the Michigan Cannabis Regulatory Agency (CRA) to conduct cannabis compliance testing. The sample space contains leaf tissue from throughout the flowering phase of growth and for a wide variety of chemovars grown under varying conditions, enabling broad generalization.

2.1 Data Collection

The samples were analyzed for elemental content using a pair of instruments; inductively coupled plasma mass spectrometry (ICP-MS) and Elementar vario macro cube. ICP-MS was utilized to measure concentrations of phosphorus (P), potassium (K), boron (B), zinc (Zn), nickel (Ni), molybdenum (Mo), manganese (Mn), magnesium (Mg), iron (Fe), copper (Cu), and calcium (Ca). Due to solvent interference, testing for nitrogen (N) and sulfur (S) was conducted using an Elementar vario macro cube as opposed to ICP-MS.

Elemental analysis has long been utilized to guide plant cultivation, as understanding the functional response of the plant to mineral nutrition is essential to positively influencing growth (12). Cannabis, specifically, is a natural accumulator of macro and trace elements, and though the introduction of these elements is environmentally dependent, their retention has been suggested to be genetically determined (6, 13, 14). Understanding this distinction is important, as mineral nutrition has been linked to the production of several secondary metabolites, including the medicinally valuable cannabinoids (15). A lack of necessary nutrition, a form of abiotic stress on the plant, has also been demonstrated to increase secondary metabolism (16, 17).

In addition to elemental data, strain identifiers unique to each cultivator were also recorded. These identifiers represent the marketed strain of the plant itself, and thus can be used as a loose representation of the samples' chemovar. Due to the inconsistency by which strains are labeled in the cannabis market, distinct strains from each cultivator were given unique identifiers (8).

2.2 Statistical Analysis

The Shapiro-Wilk test provided evidence to reject the normality assumption. Because of this, nonparametric methods were utilized in later analysis. The Kruskal-Wallis test was used to test for differences in the medians between groups (18, 19). Following this, Dunn’s test was employed with a Bonferroni correction to adjust for repeated hypothesis tests to (20). Spearman’s correlations were also calculated between pairwise groups of elements (21).

Principal components analysis (PCA) was also performed to reduce the complexity of the multivariate dataset and determine the primary sources of variance (9, 22, 23, 24). Utilizing the primary components, the K-means clustering algorithm was employed to examine groups given the unique strain identifiers (25, 26). This was done in the interest of identifying whether the samples could be grouped appropriately and if elemental composition plays an underlying role in each strain.

To better understand the specifics of the elemental distributions within the leaf samples, descriptive statistics were calculated on the measured analyte concentrations. These values are given in Table 1.

Table 1

Descriptive statistics for each analyte. Values are given in ug/g (ppm) unless otherwise specified.
	Min	Median	Mean	Max	SD	RSD (%)
N	12800.00	47200.00	44971.75	75400.00	12889.69	28.66
Ca	6163.34	41.804.06	44425.95	117215.36	15897.07	35.78
K	9767.47	26697.73	26732.15	48175.00	6316.85	23.63
Mg	2014.16	7703.00	8028.99	23306.93	2716.79	33.84
P	2372.64	5815.02	5911.25	13201.90	1485.39	25.13
S	0.00	3390.00	3421.55	23830.00	1143.53	33.42
Mn	15.11	147.80	177.52	830.50	124.15	69.93
Fe	33.99	106.58	142.33	1737.38	136.84	96.14
B	19.41	79.45	94.21	405.12	58.85	62.47
Zn	10.34	41.88	47.90	239.16	25.07	52.34
Cu	0.92	4.12	4.57	32.73	2.32	50.67
Mo	0.00	0.60	1.84	26.66	2.84	154.69
Ni	0.00	0.06	0.08	1.37	0.08	104.93

Measured concentrations range from a non-detectable minimum to a maximum of 11.7%. Of the elements measured, nitrogen, calcium, and potassium were the only three with averages greater than 1%, indicating these three elements are in greatest abundance within the plant. In contrast, manganese, iron, boron, zinc, copper, molybdenum, and nickel display average concentrations less than 0.02%, indicating these elements are consistently found in only trace amounts. The remaining elements: magnesium, phosphorus, and sulfur, fall between these groups of macro and micronutrients.

Use of the Shapiro-Wilk test provided evidence to reject the normality assumption for each of the analytes measured. The descriptive statistics in Table 1 suggest most of these analytes are skewed right, as the concentration values possess lower bounds at a concentration of zero. Due to this normality violation, nonparametric tests were used in place of traditional parametric analysis (27).

The Kruskal-Wallis test confirmed the presence of significant differences in the medians between groups (18, 19). Dunn’s test then yielded insignificant p-values (values exceeding the significance threshold of 0.05) for the pairwise relationship of both nitrogen and calcium and manganese and iron (20). These results suggest their distributions are similar. Of these, nitrogen and calcium are the two most abundant elements in the samples tested.

3.1 Correlations Among Trace Elements

Nonparametric Spearman’s correlations were computed between pairwise groups of elements. These correlation coefficients are expressed using a correlation matrix, displayed in Table 2. The four coefficients with the greatest magnitudes are bolded.

Table 2

Correlation analysis for the element concentrations of cannabis leaves. Coefficients with the four greatest magnitudes are bolded.
	N	P	K	B	Zn	Ni	Mo	Mn	Mg	Fe	Cu	Ca	S
N	1
P	0.29	1
K	0.45	0.25	1
B	-0.24	0.10	0.03	1
Zn	0.21	0.38	0.12	0.07	1
Ni	0.24	-0.06	0.23	-0.11	0.12	1
Mo	0.29	0.33	0.10	0.22	0.12	0.01	1
Mn	-0.14	0.11	-0.10	0.21	0.51	0.02	0.01	1
Mg	-0.06	0.22	0.03	0.66	0.15	-0.02	0.26	0.21	1
Fe	0.24	0.23	-0.05	-0.01	0.27	0.12	0.40	0.22	0.09	1
Cu	0.59	0.24	0.38	0.02	0.28	0.34	0.40	0.09	0.02	0.28	1
Ca	-0.13	0.09	-0.04	0.44	0.38	0.08	-0.06	0.47	0.63	0.05	-0.09	1
S	0.61	0.36	0.36	0.08	0.13	0.18	0.52	-0.12	0.09	0.25	0.52	-0.12	1

Magnesium and boron are the most heavily correlated of all the pairwise combinations (r = 0.66). Magnesium and calcium, nitrogen and sulfur, and nitrogen and copper also possess similarly moderate, positive correlations.

3.2 Principal Components Analysis

Principal components analysis (PCA) was used to reveal three distinct clusters of elemental variances within the dataset. Conducting PCA on the dataset resulted in thirteen principal components. The proportion of the variance explained by each of the components, including the cumulative proportion, is expressed by the scree plot in Fig. 1.

The first five principal components account for 69% of the total variation, yielding 24%, 18.7%, 10%, 8.2%, and 8.2% of the variance for each respectively. There is a noticeable drop after the second principal component, after which each subsequent component yields diminishing returns. Therefore, for the purpose of this analysis, only the first three principal components will be considered, as these account for over 50% of the variation. To better understand the specific relationships, present within these principal components, biplots can be constructed utilizing the loadings and scores of each analyte (22, 24). These plots are expressed in Fig. 2.

In the plot of PC1 against PC2 in Fig. 2, the loading vectors for boron, calcium, manganese, magnesium, and zinc all have small angles in respect to one other while also being relatively parallel to the PC1 axis. These elements account for the most variation in both the first principal component and the dataset. They also present the highest correlations in Table 2, providing further evidence for this relationship. Likewise, the loading vectors for nitrogen, potassium, copper, and sulfur also possess small angles in relation to one another and are relatively parallel to the PC2 axis. Molybdenum, iron, and potassium fall in between these two groupings, appearing at an angle to both axes.

The second plot of PC2 against PC3 expresses the same relationship among nitrogen, potassium, copper, and sulfur, both against one other and the second principal component. The other analytes, however, do not appear to possess strong correlations among themselves or the third principal component. This indicates much of the strong correlation among these variables exists in the first two principal components.

Finally, the third plot of PC1 against PC3 shows much of the variation existing along the first principal component. This is to be expected, as the first principal component accounts for significantly greater variation than the third. Nickel, which exerts almost no influence on the first principal components, has a loading vector nearly parallel to the third principal component in a positive direction. This stands in contrast with the plot of PC1 against PC2, where nickel was parallel to the second principal component, but in a negative direction. This indicates variation in nickel is largely accounted for by the second and third principal component rather than the first.

This analysis confirms the presence of three specific groups of elements that tend to vary together, and in contrast to other elements. The specifics of these groupings, however, cannot be established by PCA. Other clustering algorithms are necessary to further elucidate these relationships.

3.3 K-means Corroborates the PCA Clusters

The K-means clustering algorithm was utilized to identify three distinct clusters in the dataset. Three clusters were chosen as the optimal amount utilizing the silhouette method, modified to include scaled inertia (28). This agrees with the general clustering observed in the first two components of the PCA model.

Following K-means analysis, the clustering can be visualized by plotting against the first two principal components, as is expressed in Fig. 3.

Overlaying this clustering over the biplot of the first two principal components can help interpret these clusters. The first grouping, denoted in red, lies in the same direction as the loading vectors for phosphorus, sulfur, copper, potassium, nickel, and nitrogen. This indicates the first cluster will tend to possess higher values for these elements. In contrast, the second grouping, denoted in green, lies in the opposite direction of these vectors, indicating lower values for these elements. This trend is further confirmed by examining the distributions of these scaled analytes in each of these clusters, which is expressed in the first plot of Fig. 4. The distribution of the red grouping tends to fall above the other two groups for each analyte present, while the distribution of the green grouping tends to fall below the other two.

Likewise, boron, calcium, manganese, magnesium, zinc, molybdenum, and iron are all directed toward the third cluster, denoted in gray. This indicates the third cluster will tend to possess higher values for these elements. The second plot in Fig. 4 corroborates this conclusion.

3.4 Strain Assignment to K-Means Clusters

Utilizing the strain identifiers for each cultivator, frequencies of cluster assignment were determined for each distinct strain. Strains were discarded from analysis which possessed fewer than ten samples in order not to bias classifications with underrepresented strains. This resulted in 742 samples divided among twenty-three distinct strains. The percentage assignment of these samples to each of the three categories identified by the clustering algorithm are presented in Table 3. Dominant clusters are bolded.

Table 3

Strain specific groupings into K-means categories. Category colors denote the percent inclusion of each strain into the categories described in Figs. 4 and 5. Dominant clusters are bolded.
Strain Code	Sample Count	Red	Green	Grey
C1-S4	10	100	0.00	0.00
C1-S2	15	93.33	0.00	6.67
C1-S10	63	66.67	31.75	1.59
C1-S8	45	66.67	17.78	15.56
C1-S12	50	58.00	36.00	6.00
C1-S1	28	57.14	39.29	3.57
C1-S3	106	44.34	34.91	20.75
C2-S4	23	8.70	86.96	4.35
C2-S10	25	8.00	84.00	8.00
C2-S8	31	16.13	83.87	0.00
C2-S23	15	20.00	80.00	0.00
C2-S7	19	21.05	78.95	0.00
C2-S2	36	16.67	63.89	19.44
C2-S9	37	35.14	62.16	2.70
C1-S11	20	30.00	55.00	15.00
C2-S3	32	34.38	52.12	12.50
C1-S7	77	31.17	45.45	23.38
C3-S3	11	0.00	27.27	72.73
C3-S1	14	7.14	28.57	64.29
C5-S1	27	3.70	33.33	62.96
C5-S3	18	5.56	38.89	55.56
C3-S2	13	0.00	46.15	53.85
C5-S2	27	7.41	40.74	51.85

It is evident that several strains were able to be grouped almost entirely into a single cluster. As evidenced by Fig. 4, strains C2-S4, C2-S10, and C2-S8 are almost entirely contained within the first cluster, meaning they tend to have higher concentrations of copper, potassium, nitrogen, nickel, phosphorus, and sulfur in comparison to the other two groups. Likewise, strains C1-S4 and C1-S2 are almost entirely contained within the second cluster, indicating they would tend to have lower concentrations of those same elements. Strains present in the third cluster would therefore tend to possess higher values for boron, calcium, iron, magnesium, manganese, molybdenum, and zinc.

The results of this study indicate quantifiable relationships exist between trace elements in cannabis leaves which can be utilized to classify strains by common elemental compositions. This suggests certain strains possess similar compositions and can thus be characterized as the same chemovar. Though this analysis only considered elemental concentration, this same principle can apply to secondary metabolites as well.

The distribution of elemental concentrations, in addition to several pairwise correlations, found in cannabis were similar to those expressed in other agricultural plants (22). The positive association between magnesium and calcium has been well established in literature pertaining to other plant species, with some studies suggesting specific stoichiometric relationships may be at play governing the concentrations of these elements (29). To our knowledge, the remaining strong correlations among elements are not mentioned in established literature.

Three distinct groupings of analytes were identified using both PCA and the K-means clustering algorithm, possibly indicating similar modes of element incorporation among them. An underlying genetic factor may also be at work, as different strains may possess their own nutritional requirements expressed in varying trace metal concentrations. This classification may provide evidence of the underlying chemovars within the dataset characterized by common elemental makeups.

When classified based on strain identifiers, these groupings become apparent, as several strains were able to be classified almost entirely into single clusters. For instance, strains C2-S4, C2-S10, and C2-S8 are almost entirely contained within the first cluster while strains C1-S4 and C1-S2 are almost entirely contained within the second. Other strains, such as C1-S7 and C1-S3, were evenly divided among all three clusters, indicating they likely contain characteristics of all three, suggesting the presence of a hybrid chemovar.

The groupings may also be explained by varying extrinsic cultivation practices, including soil composition and fertilizer regimens. The first half of each strain code contains the cultivator identifier, of which four were utilized in this analysis. Of these four, strains from cultivator two were entirely dominant in group one while strains from cultivator one was predominantly dominant in group two. Likewise, cultivators three and five were primarily dominant in group three. The clustering of each cultivators’ strains indicates similar elemental compositions within plants produced at each facility. Given the strong influence of environmental factors on chemical composition, this outcome is not surprising. Varying growth conditions between facilities, such as fertilizer regiments and soil conditions, could explain this relationship.

Application of these models may provide a means to naively categorize cannabis strains by chemical data. In doing so, strains may be identified and classified based on chemovar, as was demonstrated in categorizing strains based on cultivators. Applying data related to secondary metabolites, such as cannabinoids, to this analysis may allow for specific high-yield strains to be identified, in addition to the common elemental profiles which result in desired outcomes. However, further research is needed to elucidate the specific relationships governing extrinsic factors, nutrient composition, and secondary metabolite concentration.

These findings illustrate the value in chemometric analysis of cannabis, an application which has received very little attention since the widespread legalization of the plant for medicinal and recreational use. Mineral elements play key roles in a variety of biochemical pathways that support plant health and generate secondary metabolites, among which are the medicinally valuable cannabinoids. The application of the methodology outlined in this study, in conjunction with additional data relating to concentrations of these key analytes, could serve to guide crop optimization toward the production or suppression of specific target analytes (15).

Principal Components Analysis (PCA)
Inductively Coupled Plasma Mass Spectrometry (ICP-MS)
Cannabis Regulatory Agency (CRA)
Cannabidiol (CBD)
Tetrahydrocannabinol (THC)

Ethics Approval and Consent to Participate

Not applicable

Consent for Publication

Not applicable

Availability of Data and Materials

The data that supports the findings of this study are available from Cambium Analytica, LLC but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Cambium Analytica, LLC.

Competing Interests

The authors declare that they have no competing interests.

Funding

Cambium Analytica, LLC

Authors’ Contributions

BL analyzed and interpreted the elemental data obtained from the cannabis products. He was also the only author of the manuscript.

Acknowledgements

Edward Szczygiel, PhD (Manuscript editing)

Alexander Adams (Manuscript editing)

Authors’ Information

BL holds a master’s degree in biostatistics from the University of Florida, along with bachelor’s degrees in mathematics and biochemistry from Lake Superior State University. He is currently the Director of Software and Data Analytics at Cambium Analytica, LLC.

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Elemental Analysis for the Optimization of Plant Secondary Metabolites Using Cannabis sativa as a Model Organism

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Abstract

Figures

1. Introduction

2. Methods

2.1 Data Collection

2.2 Statistical Analysis

3. Results

3.1 Correlations Among Trace Elements

3.2 Principal Components Analysis

3.3 K-means Corroborates the PCA Clusters

3.4 Strain Assignment to K-Means Clusters

4. Discussion

Abbreviations

Declarations

References

Additional Declarations

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Version 1