Metabolic Fingerprinting of the Lycopodiales Species for

Chapter 18

Metabolic Fingerprinting of the Lycopodiales Species for Chemotaxonomy and Quality Control Chee-Yan Choo,*,1 NorShahidah Sahidan,1 and A. Latiff2 1MedChem Herbal Research Group, Faculty of Pharmacy, Universiti Teknologi MARA, 42300 Puncak Alam, Selangor, Malaysia 2Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia *E-mail: [email protected].

Herbs are used either singly or in combination as an alternative to drugs for the treatment of a variety of diseases. The advancement of technology has enabled more stringent quality control of herbs. For example, (-) Huperzine A is an alkaloid originally isolated from Huperzia serrata and has been found to be a potent, reversible and selective acetycholinesterase inhibitor. (-) Huperzine A is found in other Lycopodiales species. The aim of this study is to develop chemical profiles of Lycopodiales species with the hplc-pda system. Chemical profiles are analyzed with the chemometrics software. Species from the Lycopodiales order were collected throughout Peninsular Malaysia. Methanolic extracts of dried club mosses were dissolved in methanol and subjected to hplc analysis. The chemical profile was acquired with a hplc-pda connected to a 250 x 4.6mm ODS-3, 3 μm column maintained at 35 °C. A gradient system was used for the separation of (-) huperzine A. Data were collected from 200-500nm. The chemical profiles were exported to the UnScramblerX software. Using the unsupervised principal component analysis, the species were clustered into two families, namely the Huperziaceae and Lycopodiaceae families. The species can be chemically

© 2014 American Chemical Society

discriminated using the principal component analysis. This methodology is useful for both the chemotaxonomic discrimination of species from the Lycopodiales order and quality control of plant material. Keywords: Lycopodiales; chemotaxonomy

metabolic

fingerprint;

Introduction Alzheimer’s disease is a neurodegenerative disorder of the central nervous system, characterized by loss of cognitive ability and severe behavior abnormalities, which ultimately results in degradation of intellectual and mental activities (1). It is associated with a selective loss of cholinergic neurons and reduced levels of acetylcholine neurotransmitter. Breakdown of acetylcholine causes decreased activity in the cerebral synapses and is a cause of cognitive impairment in the course of Alzheimer’s disease. Huperzine A (Figure 1) is an alkaloid originally isolated from H. serrata and has been found to be a potent, reversible and selective acetylcholinesterase inhibitor (2, 3). Clinically it was proven useful for Alzheimer’s disease in China and approved for mild-to-moderate stages (4, 5) and marketed in the United States as a dietary supplement (6).

Figure 1. Structure of (-) Huperzine A. There are more than 500 Lycopodiales species yet only fewer than 40 species have been studied on its alkaloidal content (7–12). Lycopodiales species is a large group of species that are commonly known as club mosses. These species are characterized by low, evergreen, coarsely moss-like and club-shaped strobili at the tips of mosslike branches. The club mosses are the oldest extant terrestrial vascular plants with origin from the late Silurian to early Devonian era. The taxonomy of the genus is still not fixed. There are more than 500 species in Lycopodiales belonging to the two main families Lycopodiaceae and Huperziaceae. Nevertheless, these plants are not abundant, grow very slowly and are only found in very specialized habitats. Since the discovery of (-) huperzine A as a potent anticholinesterase, various groups have reported the distribution of huperzine A or anticholinesterase activity in club mosses from various regions, namely Europe (13), China (14–16), Australasia and Southeast Asia (17, 18), Peninsular Malaysia (19, 20), Panama (21), Iceland (22) and northeastern India (23). 354

The objective of this study was to use the metabolite profile to discriminate the species into clusters. The Principal Component Analysis (PCA) model can be further used to classify the species or quality control of finished pharmaceutical products containing Lycopodiales species.

Methodology Sample Preparation The club mosses were collected from the Peninsular Malaysia and authenticated by Emeritus Prof. Dr. A. Latiff from the Universiti Kebangsaan Malaysia. The collected club mosses were dried at 40 °C for 24 hours or until constant weight. The process of drying, grinding and extraction is according to earlier method (20). Briefly, the powdered club mosses were extracted with methanol at 40 °C. Every hour, the extract is decanted and fresh methanol is refilled in for the next cycle of extraction. This process is repeated five cycles. The combined extracts were dried under reduced pressure with a rotary evaporator (Buchi, Switzerland) at 40 °C and further freeze dried (Labconco, USA). Stock solution of 5 mg/mL was prepared by dissolving in methanol. Chromatographic Condition The method developed with a HPLC-PDA (Waters, USA) was used for the analysis of club mosses samples (20). The oven temperature was maintained at 35 °C and the column used was a 250 x 4.6 mm ODS-3, 3um, column (Inertsil, Japan). A gradient method was used with increasing amount of acetonitrile from 20 % to 70 % in deionised water with 0.01 % TFA and the flow rate was maintatined at 1 mL/min. The photodiode array detector wavelength was set to monitor from 200-500 nm. Triplicate samples were used. (-) Huperzine A (Sigma, USA) was used as an internal standard. Principal Component Analysis (PCA) Chromatogram data extracted at 308 nm were exported to the multivariate software (UnScramblerX (Camo, Norway)). Peak areas of the dataset were normalized without further treatment of the dataset. The dataset was analysed with the unsupervised pattern recognition method, Principal component analysis (PCA). PCA is a mathematical method using orthogonal transformation to change a set of possibly correlated variables to a set of values of uncorrelated variables named principal components (PCs). PCA can highlight both similar and independent information. Dataset were subjected to the multivariate software, UnScramblerX, v10.1 (Camo, Norway).

Result The Lycopodiales species were collected from Peninsular Malaysia (Table 1). A total of 23 species were collected from the mountain. 355

Table 1. Lycopodiales species from Peninsular Malaysia Name

Collected location

1

Huperzia pinifolia Trevis

Taman negara

2

Huperzia c.f. pinifolia Trevis

Taman negara

3


Perak/Pahang border

4


Cameron Highlands

5

Huperzia phlegmaria (L.) Rothm

Perak/Pahang border

6


Cameron Highlands

7


Pahang border

8

Huperzia phyllantha (Hook.f.&Arn.) Hulob

Kelantan

9

Huperzia carinata (Desv. Ex poir) Trevis.

Cameron Highlands

10

Huperzia nummulariifolia (Blume) Jermy in T.C. Chambers & Crabbe

Pahang

11

Huperzia nummulariifolia (Blume) Jermy in T.C. Chambers & Crabbe

Perak/Pahang border

12

Huperzia tetrasticha (Kunze ex Aldrew.) Hulob

Pahang

13

Huperzia squarrosa (G. Frost) Trevis

Perlis

14

Lycopodium platyrhizoma Wilce

Cameron Highlands

15

Lycopodium platyrhizoma Wilce

Frasers Hill

16

Lycopodium casuarinoides Spring

Cameron Highlands

17


Pahang

18


Frasers Hill

19

Lycopodium clavatum L.

Pahang

20

Lycopodium clavatum L.

Cameron Highlands

21

Lycopodiella cernua (L.) Pic. Serm.

Langkawi

22


Selangor

23


Frasers Hill

Sample No.

The chromatogram dataset was integrated at 308 nm with 73 peaks (C1-C73) for all the species were used (Figure 2). The dataset was normalized with the UnScramblerX software and all the 73 peaks were used for the chemometric analysis. (-) Huperzine A was eluted as peak number C39. All the peaks were ovelayed on the similar line plot.

356

357 Figure 2. Line plot of normalized dataset.

Figure 3. PCA (i) score plot and (ii) loadings analysis of Lycopodiaceae species. The normalized dataset subjected to PCA analysis projected two clusters (Figure 3i). All the species from the genera Lycopodium and Lycopodiella were clustered into one cluster (Figure 3i). Species from the genus Huperzia were clustered into the second cluster. Since the 73 peaks were used for the PCA analysis, both Principal component (PC) 1 and 2 were only able to explained 21% of the variations. Nonetheless, the species were discriminated into two clusters, namely, Lycopodiaceae and Huperziaceae families, respectively. The metabolite profile used for the principal component analysis (PCA) showed similarity to the taxanomy classification by Ching (24) and Holub (25). Both have classified the species from Lycopodiales order into two families, namely Huperziaceae and Lycopodiaceae. However, Ollgaard (26) have combined both the families 358

with four genera, namely, Huperzia, Lycopodiella, Lycopodium and . Thus, the chemical profiles acquired with a high performance liquid chromatography coupled to the photo-diode array detector was congruent will the earlier taxanomy by Ching (24) and Holub (25). From the loading plot (Figure 3ii), influential peaks contributing to the clustering of the species were identified. All the peaks in the quadrant Q1 of the loading plot were influential peaks in the Huperzeaceae family clustering. While all the peaks on the quadrant Q2 of the loading plot were influential peaks for the clustering of the Lycopodiaceae family. No outliers were deteceted with the Hotelling T2 statistics with a significance of 99 % (Figure 4).

Figure 4. Hotelling T2 Statistics.

Conclusion The metabolite profiles from the high performance liquid chromatography coupled to a photodiode array detector were clustered into two clusters, namely the Lycopodiaceae and Huperzeaceae families. Influential peaks were identified contributing to the clusters. No outliers were detected. The PCA model maybe applied for the quality control of pharmaceutical supplements containing Lycopodiales species.

Acknowledgments The author is grateful to the Ministry of Science and Technology and Innovation for funding this study under the eScience grant, the university’s internal RIF grant and a postgraduate scholarship from MOHE. 359

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Metabolic Fingerprinting of the Lycopodiales Species for

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