Difference between revisions of "Journal:Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome"

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Chemometric models are used to evaluate metabolite datasets to delineate relationships and identify potential influences on phytochemical diversity.<ref name="TuriMeta15">{{cite journal |title=Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system |journal=Journal of Natural Products |author=Turi, C.E.; Finley, J.; Shipley, P.R. et al. |volume=78 |issue=4 |pages=953-66 |year=2015 |doi=10.1021/np500667z |pmid=25751407}}</ref><ref name="WorleyMulti13">{{cite journal |title=Multivariate Analysis in Metabolomics |journal=Current Metabolomics |author=Worley, B.; Powers, R. |volume=1 |issue=1 |pages=92–107 |year=2013 |doi=10.2174/2213235X11301010092 |pmid=26078916 |pmc=PMC4465187}}</ref><ref name="HagelPlant08">{{cite journal |title=Plant metabolomics: analytical platforms and integration with functional genomics |journal=Phytochemistry Reviews |author=Hagel, J.M.; Facchini, P.J. |volume=7 |issue=3 |pages=479–497 |year=2008 |doi=10.1007/s11101-007-9086-9}}</ref> These approaches can be classified as targeted analysis, untargeted phytochemical discovery, metabolomic profiling. or fingerprinting.<ref name="TuriMeta15" /> Targeted metabolomics determines differences in known phytochemicals, while the untargeted approaches evaluate unidentified compounds in the phytochemical profiles.<ref name="TuriMeta15" /> Targeted-untargeted approaches combine known metabolites with the untargeted datasets as a hypothesis-generating tool to discover metabolite relationships, clusters, families and biochemical pathways.<ref name="TuriMeta15" /><ref name="BrownPhyto12">{{cite journal |title=Phytochemical diversity of cranberry (Vaccinium macrocarpon Aiton) cultivars by anthocyanin determination and metabolomic profiling with chemometric analysis |journal=Journal of Agricultural and Food Chemistry |author=Brown, P.N;. Murch, S.J.; Shipley, P. |volume=60 |issue=1 |pages=261–71 |year=2012 |doi=10.1021/jf2033335 |pmid=22148867}}</ref> The use of these models and algorithms enables a better understanding of metabolite commonality and diversity within plant species.<ref name="ScherlingMeta10">{{cite journal |title=Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species |journal=PLoS One |author=Scherling, C.; Roscher, C.; Giavalisco, P. et al. |volume=5 |issue=9 |pages=e12569 |year=2010 |doi=10.1371/journal.pone.0012569 |pmid=20830202 |pmc=PMC2935349}}</ref>
Chemometric models are used to evaluate metabolite datasets to delineate relationships and identify potential influences on phytochemical diversity.<ref name="TuriMeta15">{{cite journal |title=Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system |journal=Journal of Natural Products |author=Turi, C.E.; Finley, J.; Shipley, P.R. et al. |volume=78 |issue=4 |pages=953-66 |year=2015 |doi=10.1021/np500667z |pmid=25751407}}</ref><ref name="WorleyMulti13">{{cite journal |title=Multivariate Analysis in Metabolomics |journal=Current Metabolomics |author=Worley, B.; Powers, R. |volume=1 |issue=1 |pages=92–107 |year=2013 |doi=10.2174/2213235X11301010092 |pmid=26078916 |pmc=PMC4465187}}</ref><ref name="HagelPlant08">{{cite journal |title=Plant metabolomics: analytical platforms and integration with functional genomics |journal=Phytochemistry Reviews |author=Hagel, J.M.; Facchini, P.J. |volume=7 |issue=3 |pages=479–497 |year=2008 |doi=10.1007/s11101-007-9086-9}}</ref> These approaches can be classified as targeted analysis, untargeted phytochemical discovery, metabolomic profiling. or fingerprinting.<ref name="TuriMeta15" /> Targeted metabolomics determines differences in known phytochemicals, while the untargeted approaches evaluate unidentified compounds in the phytochemical profiles.<ref name="TuriMeta15" /> Targeted-untargeted approaches combine known metabolites with the untargeted datasets as a hypothesis-generating tool to discover metabolite relationships, clusters, families and biochemical pathways.<ref name="TuriMeta15" /><ref name="BrownPhyto12">{{cite journal |title=Phytochemical diversity of cranberry (Vaccinium macrocarpon Aiton) cultivars by anthocyanin determination and metabolomic profiling with chemometric analysis |journal=Journal of Agricultural and Food Chemistry |author=Brown, P.N;. Murch, S.J.; Shipley, P. |volume=60 |issue=1 |pages=261–71 |year=2012 |doi=10.1021/jf2033335 |pmid=22148867}}</ref> The use of these models and algorithms enables a better understanding of metabolite commonality and diversity within plant species.<ref name="ScherlingMeta10">{{cite journal |title=Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species |journal=PLoS One |author=Scherling, C.; Roscher, C.; Giavalisco, P. et al. |volume=5 |issue=9 |pages=e12569 |year=2010 |doi=10.1371/journal.pone.0012569 |pmid=20830202 |pmc=PMC2935349}}</ref>
We hypothesized that the total THC and CBD content is not sufficient to distinguish strains and that a combination of targeted and untargeted chemometric approaches can be used to predict cannabinoid composition and to better understand the impact of informal breeding program and selection on the phytochemical diversity of cannabis. To investigate these hypotheses, we assembled a collection of cannabis strains sold by licensed producers in Canada primarily based on total THC/CBD content, and analyzed the strains for known cannabinoids using a previously validated analytical method<ref name="MudgeLeaner17">{{cite journal |title=Leaner and greener analysis of cannabinoids |journal=Analytical and Bioanalytical Chemistry |author=Mudge, E.M.; Murch, S.J.; Brown, P.N. |volume=409 |issue=12 |pages=3153–63 |year=2017 |doi=10.1007/s00216-017-0256-3 |pmid=28233028 |pmc=PMC5395585}}</ref> to establish clusters of similar plant materials.
We then used an untargeted metabolomics approach to identify previously uncharacterized compounds and chemical relationships. We identified five clusters of chemotaxonomically indistinguishable strains within the collection. Our results show that the variation in less abundant cannabinoids between cannabis strains was not dependent on the total THC and CBD content. These data suggest that the domestication of the cannabis germplasm has resulted in the loss of the CBDA pathway in some strains and the reallocation of resources between CBDA and THCA pathways in others.


==References==
==References==

Revision as of 18:26, 28 May 2019

Full article title Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome
Journal Scientific Reports
Author(s) Mudge, E.M.; Murch, S.J.; Brown, P.N.
Author affiliation(s) University of British Columbia, British Columbia Institute of Technology
Primary contact Email: Send message through journal website
Year published 2018
Volume and issue 8
Page(s) 13090
DOI 10.1038/s41598-018-31120-2
ISSN 2045-2322
Distribution license Creative Commons Attribution 4.0 International
Website https://www.nature.com/articles/s41598-018-31120-2
Download https://www.nature.com/articles/s41598-018-31120-2.pdf (PDF)
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Abstract

Cannabis is an interesting domesticated crop with a long history of cultivation and use. Strains have been selected through informal breeding programs with undisclosed parentage and criteria. The term “strain” refers to minor morphological differences and grower branding rather than distinct cultivated varieties. We hypothesized that strains sold by different licensed producers are chemotaxonomically indistinguishable and that the commercial practice of identifying strains by the ratio of total Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) is insufficient to account for the reported human health outcomes. We used targeted metabolomics to analyze 11 known cannabinoids and an untargeted metabolomics approach to identify 21 unknown cannabinoids. Five clusters of chemotaxonomically indistinguishable strains were identified from the 33 commercial products. Only three of the clusters produce cannabidiolic acid (CBDA) in significant quantities, while the other two clusters redirect metabolic resources toward the tetrahydrocannabinolic acid (THCA) production pathways. Six unknown metabolites were unique to CBD-rich strains and/or correlated to CBDA, and three unknowns were found only in THC-rich strains. Together, these data indicate the domestication of the Cannabis germplasm has resulted in a loss of the CBDA pathway in some strains and reallocation of resources between CBDA and THCA pathways in others. The impact of domestication is a lack of chemical diversity and loss of biodiversity in modern Cannabis strains.

Introduction

Cannabis sativa L. (marijuana) is a dioecious, annual plant from Central Asia that has been used medicinally and recreationally for thousands of years.[1] The domestication of Cannabis has included human selection, inbreeding, and cross breeding, as well as natural outcrossing and genome mixing.[1] Strains are not easily delineated by genotype, and only moderate correlations have been observed between C. indica and C. sativa ancestry. In addition, large genetic variance has been observed within identically named strains.[2][3] Standardized, highly controlled programs to breed elite varieties or cultivars by selection of phytochemical profile have been limited.[4][5] It is estimated that there are several hundred or perhaps thousands of strains of cannabis currently being cultivated in legal and illegal markets.[4] It is possible that chemically identical or very closely related plant material is being sold under several different names by different producers, with no clear definition of the concept of a “strain.”

Cannabis producers market their products based on the amounts of total THC and CBD with the assumption that the overall phytochemical composition of the material can be extrapolated from these values, but there is considerable anecdotal evidence suggesting that strains with similar THC/CBD content have different effects on human physiology.[6][7] More than 120 different cannabinoids have been described in Cannabis[8][9], with the most interesting phytochemistry found in the glandular trichomes on the flowers of the female inflorescences.[10] THC is the most researched cannabinoid, and there are 10 additional classes of cannabinoids with varying chemical structures.[8] Cannabinoids are synthesized in acidic forms through the condensation of geranyl diphosphate (GPP) and most commonly olivetolic acid, products of the methylerythritol phosphate (MEP) and polyketide pathways.[11][12] There are several other polyketides that can be used in place of olivetolic acid, which contribute to the wide variation within this chemical class.[13][14] Neutral cannabinoids are products of decarboxylation from processing and handling harvested flowers.

Chemometric models are used to evaluate metabolite datasets to delineate relationships and identify potential influences on phytochemical diversity.[15][16][17] These approaches can be classified as targeted analysis, untargeted phytochemical discovery, metabolomic profiling. or fingerprinting.[15] Targeted metabolomics determines differences in known phytochemicals, while the untargeted approaches evaluate unidentified compounds in the phytochemical profiles.[15] Targeted-untargeted approaches combine known metabolites with the untargeted datasets as a hypothesis-generating tool to discover metabolite relationships, clusters, families and biochemical pathways.[15][18] The use of these models and algorithms enables a better understanding of metabolite commonality and diversity within plant species.[19]

We hypothesized that the total THC and CBD content is not sufficient to distinguish strains and that a combination of targeted and untargeted chemometric approaches can be used to predict cannabinoid composition and to better understand the impact of informal breeding program and selection on the phytochemical diversity of cannabis. To investigate these hypotheses, we assembled a collection of cannabis strains sold by licensed producers in Canada primarily based on total THC/CBD content, and analyzed the strains for known cannabinoids using a previously validated analytical method[20] to establish clusters of similar plant materials.

We then used an untargeted metabolomics approach to identify previously uncharacterized compounds and chemical relationships. We identified five clusters of chemotaxonomically indistinguishable strains within the collection. Our results show that the variation in less abundant cannabinoids between cannabis strains was not dependent on the total THC and CBD content. These data suggest that the domestication of the cannabis germplasm has resulted in the loss of the CBDA pathway in some strains and the reallocation of resources between CBDA and THCA pathways in others.

References

  1. ↑ 1.0 1.1 Clarke, R.C.; Merlin, M.D. (2016). "Cannabis Domestication, Breeding History, Present-day Genetic Diversity, and Future Prospects". Critical Reviews in Plant Sciences 35 (5–6): 293–327. doi:10.1080/07352689.2016.1267498. 
  2. ↑ Sawler, J.; Stout, J.M.; Fardner, K.M. et al. (2015). "The Genetic Structure of Marijuana and Hemp". PLoS One 10 (8): e0133292. doi:10.1371/journal.pone.0133292. PMC PMC4550350. PMID 26308334. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4550350. 
  3. ↑ Soler, S.; Gramazio, P.; Figàs, M.R. et al. (2017). "Genetic structure of Cannabis sativa var. indica cultivars based on genomic SSR (gSSR) markers: Implications for breeding and germplasm management". Industrial Crops and Products 104: 171–78. doi:10.1016/j.indcrop.2017.04.043. 
  4. ↑ 4.0 4.1 Small, E. (2015). "Evolution and Classification of Cannabis sativa (Marijuana, Hemp) in Relation to Human Utilization". The Botanical Review 81 (3): 189–294. doi:10.1007/s12229-015-9157-3. 
  5. ↑ de Meijer, E. (2014). "Chapter 5: The Chemical Phenotypes (Chemotypes) of Cannabis". In Pertwee, R.. Handbook of Cannabis. Oxford Scholarship Online. pp. 89–110. doi:10.1093/acprof:oso/9780199662685.003.0005. ISBN 9780199662685. 
  6. ↑ McPartland, J.M.; Russo, E.B. (2001). "Cannabis and Cannabis Extracts". Journal of Cannabis Therapeutics 1 (3–4): 103–32. doi:10.1300/J175v01n03_08. 
  7. ↑ Russo, E.B. (2011). "Taming THC: potential cannabis synergy and phytocannabinoid-terpenoid entourage effects". British Journal of Pharmacology 163 (7): 1344–64. doi:10.1111/j.1476-5381.2011.01238.x. PMC PMC3165946. PMID 21749363. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC3165946. 
  8. ↑ 8.0 8.1 ElSohly, M.a.; Gul, W. (2014). "Chapter 1: Constituents of Cannabis sativa". In Pertwee, R.. Handbook of Cannabis. Oxford Scholarship Online. pp. 3–22. doi:10.1093/acprof:oso/9780199662685.003.0001. ISBN 9780199662685. 
  9. ↑ Turner, C.E.; ElSohly, M.A.; Boeren, E.G. (1980). "Constituents of Cannabis sativa L. XVII. A review of the natural constituents". Journal of Natural Products 43 (2): 169-234. PMID 6991645. 
  10. ↑ Turner, J.C.; Hemphill, J.K.; Mahlberg, P.G. (1978). "Quantitative determination of cannabinoids in individual glandular trichomes of Cannabis sativa L. (Cannabacaea)". American Journal of Botany 65 (10): 1103–06. doi:10.1002/j.1537-2197.1978.tb06177.x. 
  11. ↑ Flores-Sanchez, I.J.; Verpoorte, R. (2008). "Secondary metabolism in cannabis". Phytochemistry Reviews 7 (3): 615–639. doi:10.1007/s11101-008-9094-4. 
  12. ↑ Fellermeier, M.; Eisenreich, W.; Bacher, A. et al. (2001). "Biosynthesis of cannabinoids. Incorporation experiments with (13)C-labeled glucoses". European Journal of Biochemistry 268 (6): 1596-604. PMID 11248677. 
  13. ↑ Degenhardt, F.; Stehle, F.; Kayser, O. (2017). "Chapter 2: The Biosynthesis of Cannabinoids". In Preedy, V.R.. Handbook of Cannabis and Related Pathologies. Elsevier. pp. 13–23. doi:10.1016/B978-0-12-800756-3.00002-8. ISBN 9780128007563. 
  14. ↑ Shoyama, Y.; Hirano, H.; Nishioka, I. (1984). "Biosynthesis of propyl cannabinoid acid and its biosynthetic relationship with pentyl and methyl cannabinoid acids". Phytochemistry 23 (9): 1909–12. doi:10.1016/S0031-9422(00)84939-0. 
  15. ↑ 15.0 15.1 15.2 15.3 Turi, C.E.; Finley, J.; Shipley, P.R. et al. (2015). "Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system". Journal of Natural Products 78 (4): 953-66. doi:10.1021/np500667z. PMID 25751407. 
  16. ↑ Worley, B.; Powers, R. (2013). "Multivariate Analysis in Metabolomics". Current Metabolomics 1 (1): 92–107. doi:10.2174/2213235X11301010092. PMC PMC4465187. PMID 26078916. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4465187. 
  17. ↑ Hagel, J.M.; Facchini, P.J. (2008). "Plant metabolomics: analytical platforms and integration with functional genomics". Phytochemistry Reviews 7 (3): 479–497. doi:10.1007/s11101-007-9086-9. 
  18. ↑ Brown, P.N;. Murch, S.J.; Shipley, P. (2012). "Phytochemical diversity of cranberry (Vaccinium macrocarpon Aiton) cultivars by anthocyanin determination and metabolomic profiling with chemometric analysis". Journal of Agricultural and Food Chemistry 60 (1): 261–71. doi:10.1021/jf2033335. PMID 22148867. 
  19. ↑ Scherling, C.; Roscher, C.; Giavalisco, P. et al. (2010). "Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species". PLoS One 5 (9): e12569. doi:10.1371/journal.pone.0012569. PMC PMC2935349. PMID 20830202. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC2935349. 
  20. ↑ Mudge, E.M.; Murch, S.J.; Brown, P.N. (2017). "Leaner and greener analysis of cannabinoids". Analytical and Bioanalytical Chemistry 409 (12): 3153–63. doi:10.1007/s00216-017-0256-3. PMC PMC5395585. PMID 28233028. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC5395585. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Some grammar and punctuation was cleaned up to improve readability. In some cases important information was missing from the references, and that information was added.

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