PDF(Pubmed)
Abstract：
BACKGROUND: The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.
OBJECTIVE: Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.
METHODS: Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).
RESULTS: The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.
CONCLUSIONS: These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.
OBJECTIVE: Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.
METHODS: Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).
RESULTS: The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.
CONCLUSIONS: These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.
摘要：
背景：大麻的化学分类通常仅限于大麻素含量，而大麻包含不同的化学类别，其所有品种之间的丰度不同。因此，忽略大麻菌株中的其他化学类别会导致对可能导致化学复杂性和由此产生的植物药用品质的元素的有限和有偏见的理解。
目标：因此，在这里，我们报告了一项计算代谢组学研究，以阐明大麻素以外的大麻代谢图。
方法：使用基于质谱的计算工具来挖掘和评估两种大麻品种的甲醇叶和花提取物：健忘症浑浊（AMNH）和皇家荷兰奶酪（RDC）。
结果：结果显示存在不同类别的化合物，包括大麻素，但将其扩展到不同分布在品种植物组织中的类黄酮和磷脂，其中苯丙素超类在叶子中比花中更丰富。因此,根据植物组织的整体化学含量来区分两个品种，其中观察到AMNH在类黄酮含量中占主导地位，而RDC在类脂分子中占主导地位。此外,计算机分子对接研究与生物测定研究相结合表明，由于阐明的化学概况，这两个品种的抗癌特性可能不同。
结论：这些发现突出了大麻菌株中大麻素以外的独特化学特征。这种对大麻代谢组学景观的新颖映射提供了对植物生物化学的可行见解，并证明了选择某些品种用于药用的合理性。
目标：因此，在这里，我们报告了一项计算代谢组学研究，以阐明大麻素以外的大麻代谢图。
方法：使用基于质谱的计算工具来挖掘和评估两种大麻品种的甲醇叶和花提取物：健忘症浑浊（AMNH）和皇家荷兰奶酪（RDC）。
结果：结果显示存在不同类别的化合物，包括大麻素，但将其扩展到不同分布在品种植物组织中的类黄酮和磷脂，其中苯丙素超类在叶子中比花中更丰富。因此,根据植物组织的整体化学含量来区分两个品种，其中观察到AMNH在类黄酮含量中占主导地位，而RDC在类脂分子中占主导地位。此外,计算机分子对接研究与生物测定研究相结合表明，由于阐明的化学概况，这两个品种的抗癌特性可能不同。
结论：这些发现突出了大麻菌株中大麻素以外的独特化学特征。这种对大麻代谢组学景观的新颖映射提供了对植物生物化学的可行见解，并证明了选择某些品种用于药用的合理性。
