Key words
Cannabaceae -
Cannabis sativa
- metabolomics - terpenes - headspace GC-MS - entourage
Introduction
Until recently, informal breeding has been the only source of Cannabis varieties (chemovars), where normal crop breeding protocols have not been strictly
followed [1], [2], [3]. Key selection criteria used to select Cannabis chemovars included aroma, morphology, and other visual cues that some growers associated
with tetrahydrocannabinol (THC) potency [1], [2]. The aroma of chemovars has been shown to play a significant role in the selection,
preference, and quality indication of chemovars [4], [5]. The resultant marijuana chemovars may not be genetically distinct from one another
but can have different chemistry, and some chemovars with the same name are not genetically
similar [6], [7]. Genetic variation within chemovars is highlighted by the expression of phytochemicals
present, and terpenes are one of the major classes of compounds responsible for aroma,
and, therefore, are impacted by these breeding practices.
As with many high value products such as wine and hops, the variation in aroma notes
is the result of variation in the volatile constituents, including monoterpenes and
sesquiterpenes [5], [8], [9]. Terpenes are particularly interesting in Cannabis because they are sequestered in glandular trichomes and co-accumulate with the cannabinoids.
Both terpenes and cannabinoids are derived from the same precursor molecule, geranyl
pyrophosphate, and more than 240 different cannabinoids and terpenes have been described
in Cannabis
[10], [11], [12], [13]. Recent data has signified the presence of several terpene synthases in Cannabis, mainly producing the major monoterpenes and sesquiterpenes identified in Cannabis
[14].
Plant metabolomics provides the ability to study small molecules within samples to
understand the underlying impacts of genetics, environment, or stressors [15], [16]. Approaches can be used to combine information from targeted and untargeted metabolites
to discover relationships, clusters, families, biochemical pathways, genetic expression,
and post-translational modifications that would be missed when performing univariate
analysis of single metabolites [16], [17], [18], [19]. Several data reduction strategies and unsupervised classification techniques have
been developed that reduce complex phytochemical diversity issues like those found
in Cannabis chemovars and can be used to identify relationships between metabolites [17], [20], [21]. Multivariate statistics provide avenues to explore these relationships, which have
recently been used to describe the impacts of domestication and breeding on cannabinoid
biosynthesis but has not been used to evaluate terpene biosynthesis [22].
It has been suggested that Cannabis breeders selected for scent notes that they believe are indicative of high potency
chemovars. We hypothesize that this process of selection for scents believed to be
related to specific THC levels has resulted in modified terpene biosynthesis in these
chemovars. To investigate this hypothesis, we assembled a collection of 33 Cannabis chemovars from 5 different producers and profiled the terpenes. Previous analysis
had classified the chemovars as THC-dominant or cannabidiol (CBD)-THC hybrid chemovars
[22]. Our data indicate that there are groups of terpenes with characteristic aromas
that are associated with major cannabinoid content, which was the major focus of many
clandestine breeding programs.
Results
A total of 67 terpenes were detected and comprised 29 monoterpenes and 38 sesquiterpenes.
Monoterpenes accounted for 87.1 to 99.5% of the terpene profiles, while sesquiterpenes
accounted for the remaining 0.5 to 12.9%. Four chemovars had less than 1% sesquiterpenes,
while the average content was 5.4%.
The classification system based on cannabinoid content, which has been used to highlight
breeding based on cannabinoid potency described by Mudge et al. was used to identify
relationships of different terpenes across the classes [22]. These classes are summarized in [Table 1]. To assess the relationships between THC, CBD, and the terpenes, each terpene was
graphed according to THC content from lowest to highest and color coded to chemovar
class [22]. Five monoterpenes and seven sesquiterpenes were ubiquitous across all chemovars
([Figs. 1] and [2]). Three monoterpenes, limonene, β-myrcene, and α-pinene, were abundant in the majority of chemovars, while the two most abundant sesquiterpenes,
caryophyllene and humulene, ranged from 0.2 to 5.5% and 0.3 to 1.5% respectively.
Table 1 Chemovars of Cannabis were clustered into five distinct groups that could be separated by their CBD and
THC contents.
Group
|
Color code
|
CBD range (% w/w)
|
THC range (% w/w)
|
# Chemovars
|
A
|
Blue
|
< MDL – 0.08
|
11.3 – 19.1
|
20
|
B
|
Purple
|
< MDL – 0.02
|
8.0 – 9.9
|
3
|
C
|
Orange
|
7.1 – 9.7
|
5.0 – 6.7
|
6
|
D
|
Green
|
5.3 – 8.8
|
1.7 – 3.1
|
3
|
E
|
Red
|
16.1
|
0.7
|
1
|
Fig. 1 Monoterpene profiles identified as those present across the entire dataset. a Camphene, b D-limonene, c β-myrcene, d α-pinene, and e β-pinene.
Fig. 2 Sesquiterpene profiles identified as those present across the across the entire dataset.
a β-Caryophyllene, b guaia-3,9-diene, c α-guaiene, d humulene, e β-maaliene, f selina-3,7(11)-diene, and g valencene.
Seventeen terpenes were found in chemovars from a range of cannabinoid groupings,
but not in all chemovars (Fig. 1S, Supporting Information). β-Cubebene was found in all chemovars except the very low THC, high CBD chemovar (Fig. 1Sd, Supporting Information). There were considerable correlations among the lower abundance
sesquiterpenes with correlation coefficients above 0.8, as visually represented in
[Fig. 3]. Correlations were observed between γ-muurolene, copaene, β-cubebene, elemol, germacrene A, guaia-3,9-diene, β-maaliene, γ-maaliene, selina-3,7(11)-diene, α-selinene, and δ-selinene ([Fig. 3]), for which many of these metabolites were observed in either all or almost all
cannabinoid chemovars and clusters (Fig. 1S, Supporting Information).
Fig. 3 Pearson correlations between monoterpenes and sesquiterpenes within the Cannabis dataset.
Eight sesquiterpenes and one monoterpene were present in THC-dominant chemovars ([Fig. 4] and Fig. 2S, Supporting Information) and four that were found to be in chemovars identified as
mid-range THC (Fig. 3S, Supporting Information). (Z,Z)-α-Farnesene was found only in the chemovar CAN36, and β-sesquiphellandrene was found only in the chemovar CAN27 ([Fig. 4 F, H]). δ-Cadiene and an unidentified sesquiterpene were found only in one chemovar, CAN23
(Fig. 3S, Supporting Information). Santolina triene (tentative identification) was one of
two monoterpenes observed to have correlations with sesquiterpenes sesquiterp-1 (unidentified)
and δ-cadinene, all present in this grouping ([Fig. 3] and Fig. 3S, Supporting Information).
Fig. 4 Sesquiterpene profiles present primarily in THC-dominant chemovars. a α-Amorphene, b caryophyllene oxide, c α-cubenene, d β-elemene, e γ-elemene, f (Z,Z)-α-farnesene, g germacrene B, and h β-sesquiphellandrene.
There were 18 terpenes present in high abundance in chemovars identified as high THC
and mid-level THC/CBD. Ten monoterpenes were identified as strongly correlated to
one another, and are shown in [Fig. 5]. The remaining eight monoterpene and sesquiterpene profiles observed in this group
are summarized in Fig. 4S, Supporting Information. Terpinolene was the most dominant monoterpene, which was
less than 0.3% in 27 of the 33 chemovars, but ranged from 13.4 to 41.2% in the six
chemovars that have this distinctive monoterpene profile: CAN16, CAN17, CAN19, CAN21,
CAN32, and CAN33. Terpinolene was correlated with other monoterpenes, such as α-thujene, α-phellandrene, 3-carene, α-terpinene, p-cymene, β-phellandrene, α-terpinene, and terpinen-4-ol, with correlation coefficients ranging from 0.95 to
0.99 ([Fig. 3]). Two sesquiterpene alcohols were also classed in this group and were highly correlated
to one another (Fig. 4S, Supporting Information).
Fig. 5 Monoterpene profiles representing a unique group of terpenes that dominate both THC-dominant
and CBD-THC hybrid chemovars found to be strongly correlated with terpinolene. a 3-Carene, b p-cymene, c p-cymenene, d α-phellandrene, e β-phellandrene, (f) α-terpinene,
g γ-terpinene, h terpinen-4-ol, i terpinolene, and j α-thujene.
The final three monoterpenes and four sesquiterpenes were predominantly found in CBD-containing
chemovars (Fig. 5S, Supporting Information). Two sesquiterpene alcohols, guaiol and 10-epi-γ-eudesmol, were highly correlated to one another ([Fig. 3]).
The aromas that describe each of the terpenes detected and identified in this collection
were compiled from published sources [23] and are grouped according to their presence within the terpene groupings ([Table 2]). The aromas range from pine and woody to spicy, floral, and citrus. While Group
1 terpenes are present in all chemovars and invoke most of the major aromas, Groups
2 to 5 are considered undertones, contributing to unique aromas within each terpene
cluster. Group 2 is a combination of woody, floral, and herbal undertones. Group 3,
which is found only in THC-dominant chemovars, contained herbal, floral, woody, sweet,
and spicy undertones. Group 4 appears to have a considerable amount of citrus, woody,
musty, floral, and sweet undertones and Group 5, which has the CBD-dominant chemovars,
has primarily citrus, tropical, and sweet undertones.
Table 2 Aroma descriptors for each of the terpenes identified within the Cannabis chemovars grouped based on their presence within the cannabinoid classes.
Group
|
Terpene
|
Scent descriptors
|
Aroma
|
Group 1
|
α-pinene
|
woody/pine
|
woody, pine, citrus, spicy, floral
|
β-pinene
|
woody/pine
|
trans-2-pinanol
|
pine
|
camphene
|
woody/camphor
|
α-gurjunene derivative
|
woody/balsamic
|
β-maaliene
|
woody
|
selina-3,7(11)-diene
|
possible woody
|
camphene hydrate
|
woody/camphor
|
α-bergamotene
|
woody
|
4,11-selinadiene
|
woody
|
endo-borneol
|
camphor
|
fenchone
|
camphor
|
Z-sabinene hydrate
|
balsam
|
γ-gurjunene
|
musty
|
β-myrcene
|
spicy/balsamic/peppery
|
caryophyllene
|
spicy/cloves/roses
|
copaene
|
spicy/honey
|
γ-muurolene
|
spicy
|
D-limonene
|
citrus
|
α-terpineol
|
citrus
|
β-cubebene
|
citrus
|
valencene
|
citrus
|
guaia-3,9-diene
|
floral, rose, geranium
|
germacrene A
|
floral
|
ylangene
|
ylang ylang
|
humulene
|
hoppy
|
α-selinene
|
celery
|
exo-fenchol
|
basil
|
β-selinene
|
celery
|
Group 2
|
α-gurjunene
|
woody/balsamic
|
floral, woody, herbal
|
santolina triene
|
floral
|
sesquiterp-1
|
n/a
|
δ-cadinene
|
herbal/thyme
|
Group 3
|
α-amorphene
|
woody
|
herbal, woody, floral, citrus
|
caryophyllene oxide
|
spicy, woody/carrot
|
germacrene B
|
floral/roses
|
γ-elemene
|
floral
|
2-carene
|
sweet
|
(Z,Z)-α-farnesene
|
citrus
|
α-cubenene
|
herbal
|
β-elemene
|
herbal
|
β-sesquiphellandrene
|
herbal/oregano
|
Group 4
|
α-thujene
|
woody/frankincense
|
citrus, woody, sweet, spicy
|
α-terpinene
|
woody
|
cis-β-terpineol
|
woody
|
α-santalene
|
woody
|
α-bulnesene
|
Patchouli
|
α-fenchene
|
camphor
|
cis-β-farnesene
|
citrus/sweet
|
p-cymene
|
citrus/sweet
|
α-phellandrene
|
citrus/pepper
|
γ-terpinene
|
citrus
|
terpinolene
|
sweet/pine/citrus
|
linalool
|
citrus/floral/sweet
|
δ-selinene
|
floral
|
3-carene
|
sweet
|
α-eudesmol
|
sweet
|
terpinen-4-ol
|
peppery/musty/sweet
|
p-cymenene
|
spicy/cloves
|
β-phellandrene
|
minty
|
bulnesol
|
spicy
|
Group 5
|
alloaromadendrene
|
woody
|
citrus, woody, sweet, tropical
|
guaiol
|
rose wood
|
10-epi-γ-eudesmol
|
sweet
|
cis-α-bisabolene
|
citrus/myrrh/balsamic
|
cis-β-ocimene
|
citrus/tropical
|
trans-β-ocimene
|
citrus/tropical
|
sabinene
|
citrus/pine/spicy
|
A principal component analysis (PCA) was performed in the autoscaled terpene profiles
to evaluate the clustering and multivariate correlations between the metabolites.
The PCA is shown in [Fig. 6 A]. The first two principal components (PCs) describe 47.53% of the variance within
the data. There is no clear clustering of the chemovars according to their THC/CBD
classifications as all five cluster groups overlap significantly. Based on the loading
plots of the first two PCs ([Fig. 6 B]), the majority of the sesquiterpenes cluster together in the top right quadrant
of the plot, while the terpinolene-correlated monoterpenes appear to cluster separately
from the Cannabis groups in the top left quadrant. PC2 appears to have some influence by different
monoterpenes; α-pinene and β-myrcene are negatively correlated from the terpinolene-correlated terpenes on this
PC.
Fig. 6 PCA of the monoterpene and sesquiterpene profiles for the Cannabis dataset. Chemovars are classified according to their THC/CBD contents. a Principal component (PC) 1 and PC2 b loading plots.
It was previously noted than many of the monoterpenes and sesquiterpenes were identified
across every cannabinoid class. Therefore, a data reduction strategy was undertaken
to remove these metabolites and identify any unique clustering of the chemovars when
removing these terpenes. In this case, the number of metabolites was reduced from
67 to 38 and then subjected to PCA (Fig. 6S, Supporting Information). The first two PCs of this reduced dataset describe 40.02%
of the data. The loading plots indicate that the first PC is clearly influenced by
the terpinolene-correlated monoterpenes, for which the chemovars all cluster together
on the right side of the scores plot. PC2 appears to cluster a few chemovars on the
top left and bottom left quadrant from the majority of the remaining chemovars. These
are influenced by the contents of several sesquiterpenes. The chemovars in the top
left quadrant are impacted by δ-selinene, germacrene B, α-cubenene, and γ-elemene, all metabolites identified to be present only in THC-dominant chemovars.
Discussion
We hypothesized that the practice of selecting Cannabis chemovars by aromas thought to be indicative of THC content would result in a set
of common scent tones characteristic of high-THC chemovars, and that the comprehensive
and sensitive analysis of terpene profiles in Cannabis chemovars could then provide new insights into the impact of the domestication on
Cannabis. Anecdotal evidence suggests that the informal breeding history of the crop predicted
the potency of THC chemovars based on slight aromatic undertones and breeders selected
for CBD-containing chemovars by choosing to clone individuals with specific aromas
believed to predict these metabolites [1], [3]. Cannabis aromas play many roles in chemovar selection, euphoria, and product quality, and
are strongly associated with clandestine breeding [1], [4]. Many of the terpenes have similar characteristic aromas, which can be impacted
by concentration, synergy with other aromatic compounds, and subjective interpretation
of the aroma [24]. Subjective interpretation of “desirable” Cannabis aromas during breeding could impact terpene profiles, and many chemovar names are
indicative of their aroma. Several dominant aromas described in Cannabis names include lemon, sour, skunk, berry/fruit, diesel, or cheese. There has been
considerable variation observed between the chemovar name and composition, suggesting
that some chemovar names may not accurately describe aroma due to phytochemical variance
[25].
Headspace GC-MS analysis was employed for the profiling of monoterpenes and sesquiterpenes
in Cannabis because of its sensitivity in comparison to solvent extraction methods and the ability
to highlight the aromatic expression (headspace) of the chemovars. This method detected
67 metabolites identified with reference standards and the NIST spectral database
with considerable matching capabilities. In many previous characterizations of Cannabis, the number of terpenes ranged from 14 to 37, focusing only on high abundance terpenes
and leaving a considerable number not evaluated [25], [26], [27], [28], [29]. Over 120 different terpenes have been previously detected in Cannabis, but many of those not detected in this study are typically present in trace levels
[29]. The implementation of this more sensitive technique provides a deeper insight into
the phytochemical variation within chemovars and the underlying variances that would
otherwise be overlooked in traditional solvent extraction-based methods. Evaluating
terpene profiles and potential aromatic characteristics provides a deeper insight
into aroma selection by breeders and patients [4].
Positive correlations were observed of many low abundance terpenes with the cannabinoid
classes. High-THC chemovars had a higher prevalence of herbal and floral undertones
and a higher prevalence of several sesquiterpenes. Interestingly, caryophyllene oxide
was correlated with high-THC chemovars and is a sesquiterpene identified by canine
enforcement officers to detect drugs [30]. The CBD-containing chemovars were higher in citrus and tropical undertones, which
were attributed to several monoterpenes and sesquiterpene alcohols. Aromas are determined
based on volatility, threshold, concentration, and interactions with other aromatic
compounds, therefore, the data described are only a preliminary estimation of aromatic
characteristics from each compound [24].
Cannabis chemovars with similar THC/CBD contents exhibit varying pharmacological effects [3], [22], [29], [31], and previous authors have proposed an “entourage effect” theory, suggesting that
cannabinoids and terpenes act synergistically to invoke varying pharmacological effects
[5], [31], [32]. There are over 30 000 known terpenes in plants [23]. A summary of the pharmacological activities of terpenes identified in this collection
that have been described in the literature through in vitro, in vivo, and clinical studies are presented in [Table 3]. Major monoterpenes such as α-pinene, β-myrcene, and limonene have been shown to have anti-inflammatory, analgesic, and sedative
properties evaluated in animal models, respectively [33], [34], [35], [36] ([Table 3]). Terpinolene, present in high abundance in only a select few chemovars, also showed
anti-inflammatory and sedative properties in animal models [37], [38] ([Table 3]). Minor terpenes may also play a significant role. Linalool has been shown to have
anti-inflammatory, sedative, anxiolytic, anticonvulsant, and antidepressant activities
[31], [39]. Cymene has antinociceptive activity [40]. Terpinen-4-ol has been studied extensively for its anticonvulsant and anticancer
activities [41], [42].
Table 3 Reported activities of monoterpenes and sesquiterpenes identified in Cannabis through in vitro cell-based models, in vivo animal models, and clinical data.
Group
|
Terpene
|
Pharmacological activity
|
References
|
Group 1
|
camphene
|
expectorant
|
[64]
|
caryophyllene
|
anti-inflammatory, antinociceptive, anxiolytic, antispasmodic, antidepressant, gastroprotective
|
[47], [65], [66], [67], [68], [69]
|
fenchone
|
antinociceptive activity
|
[70]
|
humulene
|
anti-inflammatory, antitumor
|
[67], [71]
|
limonene
|
antioxidant, tumor reduction, sedative
|
[33], [34], [72], [73]
|
β-maaliene
|
sedative
|
[49]
|
β-myrcene
|
sedative, analgesic, antioxidant,
|
[36], [74], [75]
|
α-pinene
|
antinociceptive activity, anti-inflammatory, anxiolytic
|
[35], [70], [76]
|
β-pinene
|
antidepressant
|
[77]
|
α-terpineol
|
anti-inflammatory, antinociceptive, gastroprotective
|
[78], [79]
|
valencene
|
anti-melanogenesis activity, UV protectant, anti-inflammatory
|
[80], [81]
|
Group 3
|
caryophyllene oxide
|
analgesic, anti-inflammatory, tumor inhibition
|
[82], [83]
|
β-elemene
|
antitumor
|
[55]
|
β-sesquiphellandrene
|
tumor inhibition
|
[84]
|
Group 4
|
α-bulnesene
|
antiplatelet
|
[85]
|
p-cymene
|
antinociceptive, anti-inflammatory, antioxidant
|
[86], [87]
|
β-eudesmol
|
anti-inflammatory, muscle relaxant, anti-cholangiocarcinoma activity, appetite stimulation,
antiangiogenic, gastroprotective, anticonvulsant
|
[88], [89], [90], [91], [92]
|
linalool
|
antidepressant, antinociceptive, sedative
|
[77], [93], [94]
|
α-phellandrene
|
antinociceptive
|
[65]
|
γ-terpinene
|
antioxidant, antinociceptive
|
[95], [96]
|
terpinen-4-ol
|
antimicrobial, antihypertensive, anticonvulsant, tumor inhibition
|
[41], [42], [97], [98]
|
terpinolene
|
antinociceptive, anti-inflammatory, sedative
|
[37], [38]
|
Group 5
|
alloaromadendrene
|
antioxidant
|
[99]
|
cis-α-bisabolene
|
anticonvulsant
|
[57]
|
guaiol
|
anti-inflammatory
|
[100]
|
sabinene
|
antimicrobial
|
[97]
|
Data from other medicinal plants can aid in understanding the pharmacological effects
of many of the terpenes in Cannabis. For example, Salvia sp. and Ocimum santum (holy basil) are used for their analgesic, antidepressant, anxiolytic, and anti-inflammatory
activities [43], [44]. These plants have many similar terpenes including borneol, β-pinene, α-pinene, camphene α-thujene, β-caryophyllene, sabinene, limonene, p-cymene, terpiniolene, ocimene, α-cubebene, linalool, β-elemene, β-caryophyllene, α-guaiene, α-amorphene, α-humulene, isoborneol, borneol, α-selinene, β-selinene, and α-muurolene. Myrcia spp. have many similar terpenes and exhibit anti-inflammatory, antiproliferative,
and antinociceptive activities [45]. Similarly, Ocium basiclicum has reported antidepressant and anticonvulsant activities and similar terpene chemistry
[46]. Further research is needed to understand the synergy of these bioactive compounds
and the pharmacological significance for humans.
Many of the sesquiterpenes detected with the headspace method are not commonly evaluated
in Cannabis. The highly abundant and commonly evaluated sesquiterpenes present in all Cannabis chemovars include β-caryophyllene and humulene. β-Caryophyllene and humulene have anti-inflammatory properties, while β-caryophyllene has been shown to be a CB2 receptor agonist, which contributes to its
anxiolytic and antidepressant activities [5], [31], [47], [48]. The data collected indicated that β-maaliene, guaia-3,9-diene, and selina-3,7(11)-diene were present in proportions as
high as humulene in many of the chemovars and may have therapeutic potential. For
example, β-maaliene was isolated from Nardostachys chinensis and provided to mice by inhalation. Results found locomotor activity was reduced
due to its sedative effect [49]. Sedation is a common effect noted for many “indica” Cannabis chemovars. Guaia-3,9-diene is prevalent in many different plants including Atractylodes spp., Curcuma wenyujin, Blumea balsamitera, Eucalyptus spp., and Piper longum L., but has not been isolated to determine its pharmacological significance as a
single entity. Extracts of these other plants have activities including immune boosting,
digestive aid, anti-inflammation, rheumatism healing, and many more [50], [51], [52], [53]. Selina-3,7(11)-diene [also known as eudesma-3,7(11)-diene] was detected in considerable
levels in Brazilian green propolis, commonly used in folk medicine to fight infections,
but again it has not been evaluated as a single entity for pharmacological effects
[54].
Some sesquiterpenes of note include β-elemene, which has strong antitumor activity, eudesmol, which is antiangiogenic,
and bisabolene, which has anticonvulsant activity [55], [56], [57]. Bisabolene was more prevalent in CBD-containing chemovars, and there is considerable
evidence that CBD has seizure reduction activity [58]. This correlated terpene could signify a synergistic effect, suggesting the benefits
of whole plant efficacy versus isolated cannabinoids. This demonstrates the need for
in-depth metabolomic evaluations of chemovars for preclinical and clinical studies
as well as to determine relationships and generate hypotheses to explain medicinal
efficacy.
These data also contribute to our understanding of the domestication of the Cannabis crop. We have previously hypothesized that the variability in the chemotaxonomy arises
from domestication syndrome in the Cannabis genome [22], [59]. In many of the chemovars, there could have been a loss of phytochemical diversity,
as breeders emphasized aromas and pharmacological activity. Breeding selection will
impact the fitness of this genus, as these terpenes may be responsible for enhancing
pest resistance, improving pollination, or other natural survival mechanisms [60]. Efforts to expand the genetic diversity in the cultivated crop may lead to new
medicinal uses and pharmacological activities.
Several limitations of this study have been identified. Headspace analysis of the
chemovars evaluated in this dataset are a subset of those available in the marketplace,
which may have additional terpene profiles and/or aromas different from those provided
herein. Given that aromas are observed from the interaction of many volatile constituents,
there may be other metabolites responsible for the aromas of unique chemovars [24]. Aroma is a product of many factors including concentration, volatility, synergy,
etc., and therefore aroma information for each terpene identified is provided, while
the aroma of each chemovar cannot be deduced. Another consideration is that the samples
were evaluated for terpene composition quickly after receipt in order to minimize
the potential for losses due to volatility during storage, and the data collected
is limited to dried samples. There is limited information available for drying, storage,
and handling of these materials prior to receipt that may have impacted the terpene
profiles. Additionally, information pertaining to the breeding, selection, and desirable
quality attributes that growers were aiming for with each chemovar is unavailable,
and therefore assumptions based on pertinent information in the industry around desirable
attributes were used to classify strains and identify trends in terpene distributions.
The sensitive analytical method employed in this work allowed a significantly higher
number of terpenes to be detected and identified in the chemovars. This expansion
of chemical composition allowed for increased chemical characterization and identified
several low abundance terpenes associated with cannabinoid potency. The data suggest
that domestication syndrome, resulting from informal breeding and selection, has impacted
phytochemical diversity, which may be associated with the pharmacological variance
observed across chemovars. Future research is needed to understand the activities
of low abundance terpenes and synergistic effects in Cannabis chemovars and to determine the importance for medical efficacy and their roles in
plant biosynthesis.
Materials and Methods
Reagents
HPLC grade methanol and acetonitrile were purchased from VWR International. Water
was deionized and purified to 18.2 MΩ using a Barnstead Smart2Pure nanopure system
(Thermo Scientific). Ammonium formate, HPLC grade (> 99.0%), was purchased from Sigma-Aldrich
and formic acid (98%) was HPLC grade and purchased from Fisher Scientific. Cannabinoid
certified reference material standards purchased as 1 mL solutions in ampules were
purchased from Cerilliant Corp. They included tetrahydrocannabinolic acid (THCA, 1.000 mg/mL),
Δ9-tetrahydrocannabinol (THC, 1.001 mg/mL), cannabidiolic acid (CBDA, 1.000 mg/mL),
cannabidiol (CBD, 1.000 mg/mL), cannabigerol (CBG, 1.000 mg/mL), cannabichromene (CBC,
1.000 mg/mL), tetrahydrocannabivarin (THCV, 1.00 mg/mL), and cannabinol (CBN, 1.000 mg/mL).
Additional standards were purchased for peak identification from Cerilliant Corp.,
which included Δ8-THC (1.000 mg/mL), cannabidivarinic acid (CBDVA, 1.000 mg/mL), cannabidivarin (CBDV,
1.000 mg/mL), cannabigerolic acid (CBGA, 1.000 mg/mL), and cannabicyclol (CBL, 1.000 mg/mL).
All cannabinoid standards were provided in either methanol or acetonitrile. Cannabis
Terpene Mix A and Mix B containing 20 and 15 terpenes, respectively, at 2000 µg/mL
in methanol were purchased from Sigma-Aldrich. Cannabis Terpene Mix A contained α-pinene, camphene, β-pinene, 3-carene, α-terpinene, limonene, γ-terpinene, fenchone, fenchol, camphor, isoborneol, menthol, citronellol, pulegone,
geranyl acetate, α-cedrene, α-humulene, nerolidol, cedrol, and α-bisabolol. Cannabis Terpene Mix B contained β-pinene, 3-carene, p-cymene, limonene, terpinolene, linalool, camphor, borneol, α-terpineol, geraniol, β-caryophyllene, cis-nerolidol, β-eudesmol, and phytol.
Plant materials
Thirty-three chemovars of Cannabis sativa L. were purchased from five licensed producers in Canada under the Access to Cannabis for Medical Purposes Regulation (ACMPR), and laboratory analysis was performed under
a Health Canada Research License. The test samples were provided as whole or milled
flowers in 5-, 10-, and 15-gram packages and stored at room temperature until use.
Due to the legal restrictions pertaining to the storage of Cannabis chemovars, submission of voucher specimens to an herbarium was not possible, but
given the regulatory framework associated with these plants, their identify has been
confirmed as C. sativa L.
Cannabinoid analysis
The content of 32 cannabinoids was determined previously [22], [61]. In brief, ground Cannabis flowers (0.200 g) were extracted with 25 mL of 80% methanol for 15 min, followed
by centrifugation at 4500 g for 5 min and filtration with a 0.22-µm PTFE filter. Extracts were diluted to within
the calibration range using the extraction solvent and placed in the 4 °C sample holder
for same-day analysis. Chromatographic separation was performed on an Agilent 1200 UHPLC
with a Kinetex C18 100 mm × 3.0 mm, 1.8 µm column (Phenomenex) using a gradient elution
with 10 mM ammonium formate (pH 3.6) and acetonitrile with detection at 220 nm. Chemovars
were classified into five clusters based on the range of CBD/THC values determined
[22].
Evaluation of volatile constituents
Terpene profiles were determined using an in-house developed method, adapted from
a previous terpene method [13]. Immediately after grinding, Cannabis flowers (0.100 g) were added to a 20-mL gas tight headspace vial. Samples were prepared
in triplicate. Using a CTC Analytics Combi-PAL headspace autosampler, the vials were
transferred to a heated incubator at 80 °C for 15 min and agitated at 500 rpm. Next,
1000 µL of the vial headspace was injected using a syringe at 120 °C. The injector
temperature was 230 °C with at a split ratio of 10 : 1. GC analysis was undertaken
on an Agilent 7890A GC coupled to a 5975B mass spectrometer (MS). Separation was achieved
on a 20 m × 180 µm ID, 0.18 µm film thickness J&W DB-5MS column. Helium was used as
the carrier gas at a flow rate of 1.3 mL/min. The column was held at 50 °C for 3 min
followed by a ramp to 170 °C at 5 °C/min for a total run time of 27 min. MS detection
with electron impact ionization at 70 eV was used to collect mass spectra from m/z 50 to 500. The MS quad and source temperatures were 230 and 150 °C, respectively.
Chemometrics
Metabolite profiling
Terpenes were identified based on comparison of mass spectra with the NIST spectral
database (NIST 11). Additionally, retention indices were compared to published literature
to confirm elution order and identity [62]. Several monoterpene standards were also analyzed to confirm identity. Multivariate
curve resolution using SOLO+MIA software (version 8.5; Eigenvector Research) was employed
to separate coeluting monoterpenes, and peak areas were determined using the software
program R, version 3.5.2 [63]. Peaks were manually aligned based on compound identity and retention time using
Excel. Missing values (zeros) were replaced with half of the lowest value in the dataset.
Identification of metabolite relationships
Individual terpenes were plotted according to their cannabinoid profiles, previously
described by Mudge et al. to identify trends within the datasets and classify them
into unique groups [22]. Trends evaluated included those present across all chemovars, those found primarily
in THC-dominant chemovars, those present primarily in CBD-THC hybrid chemovars, and
other terpene correlations independent of cannabinoid content. Correlations between
terpenes and cannabinoids were confirmed by evaluating Pearson correlation coefficients
using the R program cor.
Multivariate classification
The data were autoscaled by mean centering and scaling to unit variance in order to
give each metabolite equal weight prior to multivariate analyses. PCA was subsequently
performed using Solo+MIA.