Introduction Methods Results and Discussion Future ...

Variation in Secondary Metabolites of the Medicinal Plant Ligusticum porteri Associated with Light Environment 1,2 1,3 1 1 Emily Mooney , Brittany Smith , Luis Lowe and Janel Owens 1University of Colorado Colorado Springs 2 Rocky Mountain Biological Laboratory. 3CSU Fort Collins

Introduction

Methods-Continued

Ligusticum porteri (Apiaceae) is a medicinal plant harvested from the wild primarily for use as an herbal cold and flu remedy [1]. The common name for this species is ‘bear root’ referring to the use of this species by bears, who will ingest and apply roots to their fur purportedly for their anti-parasitic properties [2,3]. Previous work with L. porteri demonstrated greater antibacterial activity in extracts from plants in the sun versus in the shade [4]. Our objective was to assess variation in secondary metabolites in plant extracts from varying light environments. Light can drive variation in chemicals important for plant herbivore defense, many of which also contribute to medicinal properties[5]. We used both HPLC and GC-MS to determine relative concentrations of compounds.

b

a

Results and Discussion

GC-MS Analysis We carried out GC-MS analysis of the methanol extracts using an Agilent HP-6890 gas chromatograph (Agilent Technologies, Palo Alto, CA, USA) with a DB-5ms phenyl arylene polymer capillary column (30.0 m x 0.25 mm x 0.5m, J&W Scientific, Folsom, CA) with an Agilent HP5973 mass selective detector (Ionization energy: 70 eV).

Figure 5 Plot of nonmetric multidimensional scaling. Twodimensions of variation in chemicals identified by GC-MS versus gradients of PAR. Two convergent solutions were reached after four tries; stress value was acceptable (0.20).

Figure 2 Schematic diagram of GC MS

The oven temperature was held at 60C for 5min where it was raised to 300C at 30min and held for 3min at a rate of 12C/min. The carrier gas (He) was injected at 1.0mL/min, and all plant extracts were run for 32.08min. We identified the components of plant extracts by computer matching of retention indices and mass spectra to compounds in the NIST database using AMDIS, the automated mass spectral deconvolution and identification system (National Institute of Standards and Technology, Gaithersburg, MD, USA).

Figure 6 Clustering of plants versus a vector representing increasing light (PAR). Light did not drive clustering of plants based on chemicals identified by GC-MS (NMDS1=0.0665, NMDS2=0.9978, R2= 0.1192, P=0.314)

Data Analysis

Figure 1 Typical alpine meadow (a) and aspen understory (b) habitat for L. porteri.

To test for differences between light environments, we compared concentrations (ppm) of compounds from HPLC between populations using a glm in R with p-values from the ‘anova()’ function. Because of zero-inflation among data for isovanillan, caffeic acid and elemicin, we analyzed these data as counts using the ‘zeroinfl()’ function in the pscl package. We used non-metric multidimensional scaling (NMDS) to determine plant-to-plant variation in compounds identified by GC-MS. We used the ‘metaMDS()’ function in the vegan package with Wisconsin double standardization and Bray distances The NMDS used 100 random starting configurations constrained to two-dimensional solutions. Final NMDS scores from runs with the lowest stress values were used for two-dimensional plots. We tested for variation with light (PAR) using the ‘envfit()’ function. We performed all of the analyses in R Version 3.2.5 [11].

Results and Discussion

Methods Field Collection of Roots and Extract Preparation In August of 2012, we collected whole roots from twenty replicate plants across a range of light environments typical for L. porteri (Figure 1). We stored the roots at 4⁰C until crude methanol extracts were made [Methods in 4]. We filtered the crude extracts with a sterile 0.45m cellulose-acetate syringe filter. We measured light as photosynthetically active radiation (PAR) at plant height at two times between 10:00 and 15:00 hrs with using a light meter and sensor (LI-250A & LI-191R, LI-COR, Lincoln, NE).

To assess variation in seven compounds (Table 1), we modified a previously-published HPLC technique [6]. We performed reversed-phase HPLC analyses with an auto-injector (Hewlett Packard Series 1100) outfitted with a binary pump (Agilent 1100 series) and diode array detector. Chromatographic separation was carried out at room temperature using a Restek Ultra II C18 column (5cm x 4.6mm i.d. x 3) column using two solvents: (A) acetic acidacetonitrile (0.1%) and (B) formic acid (0.1%) where the gradient elution at 1 min was 60% B to 95% B after 15 min to 60% B at 25 min. The flow rate was 1.250mL/min and the total injection volume was 4L. We recorded chromatograms at 220, 260 and 280 nm. We integrated standard peak areas for use in external standard calibration calculations that determine the relative concentrations (ppm) of these compounds found in twenty plant extracts 1. Z-Ligustulide

2. 3-Butylidenephthalide

Smooth muscle relaxation, vasodilation, insecticidal, antibacterial, antifungal, anti-inflammation and antihyperglycemic [7]

4. Isovanillin

Antianginal, antihypertensive, antispasmodic, vasodilation, serotonergic activity and anti-hyperglycemic [7]

5. Caffeic Acid Inhibits both aldehyde oxidase and mitochondrial aldehyde dehydrogenase [7]

3. Trans-Ferulic Acid Antioxidant, thrombosis and atherosclerosis preventative, cholesterol lowering, antimicrobial, anti-inflammatory, anti-cancer [7]

6. Elemicin Antioxidant and antiviral [8]

Antibacterial [9] and psychotropic [10]

Table 1 We tested for seven secondary metabolites tested using HPLC. Gallic acid is not shown

More Abundant in Sun Plants

(+) Sativene

-Sesquiphellandrene

(E,E)-Farnesol

Cyclohexane-1-methanol,3,3-dimethyl-2-(3methyl-1,3-butadienyl)

Elemicin

Trans-Ferulic Acid Z-Ligustilide Elemicin

Table 3 Qualitative results from the NMDS revealed four terpenoids that tended to vary with light environment. Terpenoids are major components of the essential oils of Ligusticum species [5].

Trans-Ferulic Acid 3-Butlidenephthalide Isovanillan

Z-Ligustilide Isovanillan

3-Butlidenephthalide

Future Work Figure 3 Representative chromatograms from sun and shade plants.

HPLC Analysis

More Abundant in Shade Plants

Population PAR (mol/m2/sec) 1.

Z-Ligustilide

2.

3-Butylidenepthalide

3.

Trans-Ferulic Acid

4.

Isovanillan

5.

Caffeic Acid

6.

Elemicin

Aspen 2 179.3 4/4 4/4 4/4 0/4 1/4 2/4

Aspen 1 731.0 4/4 4/4 4/4 1/4 0/4 0/4

Sun 3 956.8 4/4 4/4 4/4 1/4 0/4 0/4

Sun vs. Shade Difference Meadow 2 Meadow 1 in Concentration (ppm) 1383.3 1740.2 P=0.00006 4/4 4/4 P=0.4449 4/4 4/4 NA 4/4 4/4 P=0.09466 1/4 1/4 P=0.00057 1/4 0/4 P=0.32 1/4 1/4 P=0.567

Table 2 Results for six chemicals detected by HPLC among four plants sampled from five sites. In a study of L. porteri from commercial sources, Rivero et al. found compounds 1-3 in all samples they examined [6]. Because several other compounds co-eluted, we could not calculate concentrations (ppm) of 3-butylidenephthalide. Gallic acid—found in the congener Ligusticum mutellina [12]—was not present in any samples. Figure 3 We found a trend for transferulic acid concentrations to be greater in the shade than in the shade. Transferulic acid is a phenolic, which are generally greater in sun plants than in shade plants [13]. This follows the pattern of greater antibacterial activity in the shade [4].

These initial results suggest follow-up studies to broaden our conclusions: • Increased sample size (> N =20) could allow observed trends to reach level of statistical significance. Sample more plants across a range of light environments. • Measure additional environmental variables that could affect secondary metabolite composition or amount. These could include • Edaphic factors • Herbivory • Mycorrhizal colonization

Acknowledgements Rocky Mountain Biological Laboratory, Brent Wallace, Dr. Berry-Lowe and UCCS Chemistry Department

References 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13)

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