Unveiling Ontogenesis of Herbal Medicine in Plant Chemical Profiles by Infrared Macro-Fingerprinting

• Introduction
• Results and Discussion
• Materials and Methods
• Apparatus and parameters
• Materials
• Procedure
• Supporting information
• Acknowledgements
• References

Abstract

Given that harvesting time has a great impact on the quality of herbal medicine, knowing the ontogenesis in the chemical profile aspect is essential to determine the optimal harvesting season. A high-throughput and versatile approach (herbal infrared macro-fingerprinting) harmonizing with the character of herbal medicine and providing the whole chemical profile (entirety), group analogues (part), and single compounds (major components) is developed to rapidly disclose the variation rule of the full chemical profile of herbal medicine over a growing season without extraction pretreatments, and thus to determine the optimal harvesting period in respect to groups of chemical compounds using Scutellaria baicalensis as a demonstration. IR macro-fingerprints of Scutellaria baicalensis harvested in the same period have a high similarity (> 0.91) despite small variations, suggesting that IR macro-fingerprinting can faithfully reflect the spectacle of “disordered order” in nature. From Year-1 spring to Year-3 autumn, general contents (%, w/w) of total flavonoids fluctuate up and down with a maximum value in Year-2 spring, and that of saccharides is relatively stable except for the attenuation from Year-2 autumn to Year-3 spring. From Year-1 autumn to Year-2 spring, flavonoid aglycones initially produced in Scutellaria baicalensis are extensively transformed to responding flavonoid glycosides. From Year-2 spring to Year-3 autumn, flavonoid glycosides are converted back to their corresponding aglycones. The best seasons for collecting Scutellaria baicalensis with a high content of flavonoid glycosides and aglycones would be Year-2 spring and Year-3 spring, respectively.

Introduction

Herbal medicine (HM), a natural complex system consisting of hundreds of chemical compounds, is recognized as a promising alternative medicine in treating chronic diseases [1], [2], [3]. Dynamic accumulation of chemical compositions in herbal plants in different phenological seasons is persistently influenced by the surroundings, among which harvesting time has a significant impact on its quality. “Some sort of standarization must be established to control batch-to-batch variation.” [4]. Therefore, a feasible and versatile protocol for acquiring a chemical profile variation rule of HM over time and its optimal harvesting period would be essential.

Hitherto, highly sensitive and specific methods involving NMR [5], gas chromatography, liquid chromatography, mass spectrometry, and their hyphenated techniques [6] are frequently used to investigate the chemical (e.g, metabolite) profiles of HM. The acquired data has substantially extended our knowledge of their chemistry and in-depth particular metabolic mechanisms. However, as herbal medicine is a natural complex system with continuous variations, investigating only a few specific marker compounds or easily extractable and examinable compositions generally cannot actually reflect the entire quality and status of HM [7], [8]. Complicated operations, high expenses, and time-consuming tedious pretreatment processes such as extraction and purification are often involved in the above methods and subsequently hider them to be widely used in practical applications. Moreover, tedious pretreatments, particularly extraction in harsh conditions, would probably induce unexpected or unwanted deteriorations, for instance, compound decomposition or transformation [9], [10]. Therefore, it would be risky to acquire low-fidelity data which may subsequently lead to a unilateral conclusion by merely using the above methods.

Infrared (IR) spectroscopy is a rapid, direct, and easy to handle vibrational spectroscopic methodology offering molecular-specific information for virtually any sample in any state (gases, liquids, and solids) because of the informative bands contributed by the heteroatomic molecules and the versatile available sampling techniques [11], [12]. Another pronounced advantage of IR is the capability of performing both qualitative and quantitative analysis of interrogated samples [13]. IR consequently has been applied in diverse fields including analytical sciences [14], [15], [16], astronomy [17], [18], [19], biological research [20], [21], and medical diagnostics [12], [22]. Multistep IR macro-fingerprinting, a method developed based upon classical FT-IR spectroscopy [11] and consisting of FT-IR, second derivative IR, and two-dimensional correlation infrared spectroscopy (2DCOS-IR) [23], can provide the “whole” chemical information and the coexistence status of all IR active compositions in complex HM systems without extraction and a solvent [24]. IR macro-fingerprint is defined specifically to complex systems for being differentiated from the spectroscopic features of pure compounds. Changes in peak position and intensity of the spectra are relevant to the content variations of chemical compositions in the sample. Specific or groups of chemical compounds can be qualitatively and quantitatively analyzed by combining evaluation methods of infrared spectral data, hence, various HM samples could be identified and discriminated in the absence of a complete understanding of the chemical constituents. This method has demonstrated a rapid and effective approach for holographic chemical characterization from entirety to single ingredient and quality control of HM [25], [26], [27], [28].

Scutellaria baicalensis Georgi is one of the well-known medicinal plants used and cultured in European and Asian countries for ages [29]. The root of Scutellaria baicalensis Georgi (SG) has been used for the treatment of fever, tumors [30], [31], hepatitis [32], and inflammatory diseases [33], [34]. Around 300 chemical compounds have been isolated and identified from SG, among which flavonoids and saccharides are considered two major classes of compositions [35], [36].

This article attempts to establish and exemplify a versatile herbal IR macro-fingerprint method to rapidly unveil the ontogenesis of HM by revealing the variation rule of the full chemical profile of HM over time and thus to determine its optimal harvesting period in terms of groups of chemical compounds without extraction/purification procedures using SG as an example.

Results and Discussion

Herbal IR macro-fingerprint can be considered an overall accumulative spectrum of all spectra of individual components (Eq-1 & Eq-2) and thus can provide the original whole chemical image of herbs [26]. As spectral peaks for the same particular function group in different molecules are located at the same spectral region with a small variation in wavenumber, the common information for a class of chemical compounds with similar molecular structures can be derived [25]. When contents of chemical compositions vary, corresponding changes would occur to the peak profile changes of the herbal IR spectrum including the displacement of spectral peaks of specific compounds due to the interferences of other compounds. Through the dynamic chemical variation profiles of herbs over time, the “growth rhythm” of (bioactive) compounds in herbs can be pictured straightforward.

where n_c denotes the number of the compounds of molecule C; k_c,i is the number of functional group i (F_i ) in the molecule C; b_c,i,j is the weighting coefficient for the peak intensity; and e is the corrections resulting from the nonlinear interactions between molecules.

SG has more than 60 flavonoids including baicalin, baicalein, wogonoside, and wogonin ([Fig. 1]) [35]. As the typical flavonoids have the same basic chemical structural motive leading to certain collective IR absorption characteristics (collective absorption), the four bioactive flavones are used to represent the total flavonoids in SG to deliver our concept.

By referring to the IR spectra of standard compounds ([Fig. 2]), it is known that the peak around 1656 cm⁻¹ is due to the stretching vibration of C=O groups in flavones, while peaks near 1614, 1585, 1500, and 1451 cm⁻¹ are mainly contributed to by the skeletal vibration of the aromatic rings in the flavones with 1614 and 1451 cm⁻¹ partially contributed to C=C and CH₃/CH₂, respectively. The strong peaks in the region of 1200–900 cm⁻¹ are the stretching vibration of various C–O bonds of saccharides. In particular, the peaks near 1056 and 999 cm⁻¹ are predominantly attributed to the stretching vibration of C–O in cellulose while the peak around 1070 cm⁻¹ is primarily induced by the stretching vibration of glycosidic bonds (C–O-C) in flavonoid glycosides (e.g., baicalin, wogonoside). For clearer identification, second derivative infrared (SD-IR) spectra by the Savitzky-Golay polynomial fitting method have been employed to enhance an apparent spectral resolution. In the region of 1700–1400 cm⁻¹, more peaks can be observed in SD-IR spectra ([Fig. 3]) than the normal IR spectra of SG-1S and SG-2S. As indicated by the SD-IR spectra, SG-1S and SG-2S have more observable corresponding peaks too and subsequently have similar absorption characteristics to baicalin, wogonoside, baicalein, and wogonin in spite of variations induced by respective endogenous interferences. Hence, the peak assignments via normal IR spectra are verified.

Ten batches of SG harvested in the spring of Year-1 (SG-1S) have similar IR macro-fingerprints with small fluctuations observed in the region of 900–1200 cm⁻¹ (Fig. 1S). Most SGs have the strongest peak of the whole spectrum around 1059 cm⁻¹, suggesting that, in the spring of Year 1, SGs principally develop their plant framework (growth stage I, [Fig. 7]), which is mainly composed of polysaccharides, particularly cellulose. Some SGs (correlation coefficient < 0.93), however, have the strongest peak around 1070 cm⁻¹ instead, indicating that these SGs have started the blooming stage of flavonoid glycoside development (growth stage II, [Fig. 7]) when harvested. The fluctuation-like “uncertainty principle” is a common phenomenon in nature, which can be termed “consistent inconsistency”. Notwithstanding, IR macro-fingerprinting can faithfully and integrally reflect this spectacle.

In Year-2 SGs (SG-2S), the strongest peak in the region of 900–1200 cm⁻¹ has shifted to around 1070 cm⁻¹ and peaks around 1740, 1655, 1614, and 1451 cm⁻¹ have been significantly intensified (Fig. 2S). In particular, the peak intensity around 1614 cm⁻¹ is almost identical to that around 1070 cm⁻¹. It is demonstrated that the spring of Year 2 is the booming stage of flavonoid glycosides converted from their corresponding aglycones (growth stage II, [Fig. 7]).

In Year-3 SGs (SG-3S), the strongest peak of the whole spectrum has changed to around 1614 cm⁻¹ and peaks around 1655, 1614, and 1451 cm⁻¹ have been further strengthened (Fig. 3S). In contrast, peaks in the region of 900–1200 cm⁻¹ have been significantly attenuated and the strongest peak in this region has red-shifted to lower wavenumbers. It is suggested that saccharides and a part of flavonoid glycosides have been metabolized and subsequently transformed to their responding aglycones (growth stage III, [Fig. 7]). This is the reason why SG-3, called “dry baicalensis”, has a brownish-black rotten or even hollow structure [37].

Chemical profile comparisons of SGs harvested in spring and autumn of each year have been conducted as well ([Fig. 4]). Interestingly, flavonoid absorption peaks in the IR spectra of all SGs harvested in autumn (SG-1A, SG-2A, and SG-3A) have a significantly lower intensity than those SGs collected in spring (SG-1S, SG-2S, and SG-3S). As suggested by HPLC quantitative results ([Fig. 5] and [Table 1]), the content of total flavonoids (the sum content of the four representative flavones) fluctuates up and down with a maximum value in SG-2S. Specifically, the two flavonoid glycosides follow the same rule of total flavonoids while the two flavonoid aglycones are the reverse despite the more than 10-fold lower content. The trend of IR results, therefore, has been in general agreement with HPLC quantitative results of total flavonoids.

From the first year (SG-1A) to the second year (SG-2S), the content of flavonoid glycosides in SGs significantly increases by being converted from corresponding flavones coupled with saccharides ([Figs. 6] and [7]) catalyzed by flavonoid glucuronosyl/glucosyl transferases [38], [39]. From the second year (SG-2S) to the third year (SG-3A), flavonoid glycosides are converted back to their corresponding aglycones catalyzed by glucosidases/glucuronidases [40], [41], and SG-3S has higher total flavonoids than SG-2S. HPLC results, however, indicate that the sum content of four representative flavonoids in SG-3S is lower than that in SG-2S ([Fig. 5] and [Table 1]). One of the main reasons is that the major part of the resulting baicalein is consumed by being translocated to the nucleus to oxidize DNA, leading to apoptosis [40]. This is maybe the dominant endogenous factor inducing SG to be “dry baicalensis”. Therefore, an ontogenesis investigation or quality control of herbal medicine monitored by only one or a limited number of marker compounds could lead to a unilateral conclusion. A full chemical profile combined with a marker compound investigation has shown advantages on these issues. Overall, the second year spring would be the ideal harvesting time for collecting SGs with a high content (%, w/w) of flavonoid glycosides, while the third year spring would be the appropriate period for gathering SGs with enriched flavonoid aglycones and a higher content of total flavonoids. Moreover, the total content of saccharides only exhibits an evident attenuation from the second year autumn to the third year spring.

The dynamic chemical profile variation of SG in three consecutive growth years has been rapidly revealed by herbal IR macro-fingerprinting in a full image manner. FT-IR, second derivative spectra, and group-peak matching results have demonstrated that IR macro-fingerprints of SGs harvested in the same period are quite similar despite small variations, suggesting that they have common major components. From Year-1 spring to Year-3 autumn, the content (%, w/w) of total flavonoids generally fluctuates up and down, and that of saccharides is relatively stable except for the attenuation in the period between Year-2 autumn and Year-3 spring. This finding has been supported by HPLC quantitative results. For the samples examined in this study, Year-2 spring would be a well-timed period for collecting SGs with a high content (%, w/w) of flavonoid glycosides, whereas Year-3 spring would be the appropriate season for gathering SGs containing enriched flavonoid aglycones in total flavonoids with a higher content.

Since geographical location, climate, soil, and other environmental factors have severe impacts on the ontogenesis of HM including SG, the optimal harvesting time would be environment-dependent. Nevertheless, herbal IR macro-fingerprinting coupled with the “group-peak matching” technique could be a rapid, simple, and ideal protocol for analyzing the whole chemical profile (entirety), group analogues (part), and single compounds (major components) of HM and subsequently revealing their dynamic variation rules, thus the optimal harvesting periods corresponding to different HMs in diverse situations could be determined quickly using the same protocol.

By using herbal IR macro-fingerprinting as an anterior guidance and combining with other methodologies, such as LC-MS, GC-MS and NMR, herbal medicine could be comprehensively characterized as multilevel (from entirety to fraction to single ingredient) and multiplex (from major to minor to trace components) with undoubtedly high fidelity.

Materials and Methods

Apparatus and parameters

A Spectrum Two FT-IR spectrometer (Perkin-Elmer) coupled with a DTGS detector in the range of 4000–400 cm⁻¹ with a resolution of 4 cm⁻¹ was used. Spectra were recorded with 0.2 cm × s⁻¹ of OPD speed and 32 scans. H₂O and CO₂ interferences were eliminated online when scanning.

An Agilent 1200 Series liquid chromatography system (Agilent Technologies) equipped with a DAD detector and an Agilent XDB-C18 column (3.5 µm, 2.1 mm × 150 mm) was used. The sample injection volume was 2 µL, the flow rate was 0.2 mL/min, and the column temperature was maintained at 25 °C. The detection wavelength was 276 nm, and the mobile phase was gradient elution, which was mixed with 1 % glacial acetic acid (A)-acetonitrile (B). The gradient program was as follows: 21 % B (v/v) at 0–5 min, 21–22 % B at 5–12 min, 22–30 % B at 12–30 min, and 30–60 % B at 30–60 min.

Materials

The roots of SG ([Table 2], Referenced Voucher Number: 00697691, Herbarium Institute of Botany CAS) were collected from Chifeng, Inner Mogolia Autonomous Region, China and were authenticated by Professor Shou-Zhuo Li, Chengde Medical College, China. Samples were dried at 55 °C until reaching a constant weight, and then were stored in a desiccator in a cool dry place for further usage after being powdered.

Chemical standards, baicalin (99 %), baicalein (99 %), wogonin (99 %), and wogonoside (99 %), were purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). Acetonitrile and methanol (HPLC grade) were from Fisher. Acetic acid (AR grade) was obtained from Jiangsu Hanbon. Ultrapure water (18.2 MΩ) was self-prepared.

Procedure

Acquisition of IR spectra: Each sample (about 1–2 mg) was blended with KBr (100 mg), grounded into powder (200 mesh), and then pressed into a tablet with a pressure of around 10 psi followed by FT-IR measurements. All IR spectra were acquired and further processed with PE spectrum software (Version 6.3.5). Second derivative IR spectra were acquired by applying a Savitzky-Golay polynomial fitting (13-point smoothing) [42] to normal IR spectra. Spectral correlation coefficients were calculated by using the following equation:

where each infrared spectrum (a row of the spectral matrix X) is considered a vector in the n-dimensional space produced by the n variable coordinates and the similarity between two spectra is defined as the cosine of the angle between the two vectors; weighting factors (w_k ) due to noise and the absorption of water vapor and carbon dioxide are applied.

HPLC procedure: Methanol solutions containing four reference standards were prepared and diluted to a series of appropriate concentrations for the construction of calibration curves ([Table 3]). Each calibration curve was analyzed five times with eight different concentrations using the HPLC condition as described above. Chromatograms were recorded using Agilent Chemstation (Version B 04.01SP1).

Each powdered SG sample (0.5 g) was accurately weighed and refluxed with 50 mL of mixed solvent (water-ethanol-ethyl acetate-glacial acetic acid, 50 : 49 : 5 : 1, v/v/v/v) for 1.5 h. The obtained supernatant was filtered through a 0.45-µm membrane and then injected into HPLC. A typical HPLC chromatogram of SG-2S is shown in [Fig. 8] and peaks 1, 2, 3, 4 represent baicalin, wogonoside, baicalein, and wogonin, respectively.

Supporting information

Data of the IR macro-fingerprints of the batches of SG harvested at different times are available as Supporting Information.

Acknowledgements

This work is supported by the National Natural Science Foundation of China (21075076) and the grant support of the Administration of Traditional Chinese Medicine of Heibei Province, China (2011031).

Conflict of Interest

The authors declare no conflict of interest.

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Correspondence

• References

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  CrossrefPubMedSearch in Google Scholar
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