1. Introduction
Influenza, an acute viral respiratory infection, is still a global public health concern causing significant morbidity and mortality globally. Approximately 9% of the world’s population is annually affected by influenza with about half a million deaths each year [
1]. Lonicerae japonicae flos (LJF, Jinyinhua in Chinese), a well-known heat-clearing and detoxifying botanical drug, has been used to effectively treat influenza infection for thousands of years [
2,
3]. Research studies revealed that LJF decoction could suppress the replication and the release of influenza A virus [
3,
4]. Clinical trials also confirmed that prescribed herbal decoction and preparation containing LJF exhibited potentially positive effect on the influenza A strain, especially on its time to defervescence [
3]. In addition, chemical investigations revealed that more than 300 compounds have been isolated and identified in LJF, including phenolic acids, flavonoids, iridoid glycosides, volatile oils and others. Among them, phenolic acids, flavonoids and iridoid glycosides were reported to possess inhibitory activity against neuraminidase (NA), thereby blocking the release of influenza virus [
5,
6,
7,
8,
9]. However, limited research has been performed to systematically screen the bioactive components in LJF against influenza.
On the other hand, the quality and anti-influenza virus activity of LJF can be affected by many factors such as cultivation pattern, geographical origin and processing method. Firstly, cultivated and wild LJF samples exhibited different macroscopic, microscopic characteristics and chemical compositions, which could lead to efficacy variance [
10]. Next, the environment of its geographical origin displayed an impact on the content of bioactive compounds and even clinical effectiveness [
11,
12]. Finally, the post-harvest processing method is also one of the main factors affecting the quality of dried herbs, including the organoleptic and chemical properties, as well as the medical efficacy and safety [
13]. Hot-air dried LJF showed a fine green appearance in color, while sun dried LJF was yellow [
14]. The primary and secondary metabolites were different as well in diverse drying methods [
15]. However, the impact of quality-affecting factors on anti-influenza activity and on the quality of LJF was lacking a comprehensive investigation.
Chemical pattern recognition is a comprehensive and effective means for the quality assessment of traditional Chinese medicine [
16,
17], which applies chemical data and pharmacological effect information to screen bioactive components and then establish quality evaluation models [
18]. In the current study, a chemical pattern recognition strategy integrating an ultrahigh-performance liquid chromatography (UHPLC) fingerprint and anti-influenza virus activity was established to assess the holistic quality of LJF and investigate the impact of the quality-affecting factors on the anti-influenza activity of LJF. Firstly, the UHPLC fingerprints and anti-influenza virus activity of 71 batches of LJF samples were obtained. Secondly, orthogonal partial least squares (OPLS) analysis, Pearson correlation analysis and grey relational analysis (GRA) were applied to screen out the bioactive compounds with anti-influenza virus activity. Finally, chemical pattern recognition models were established based on bioactive compounds by linear discriminant analysis (LDA) to evaluate the quality and efficacy of LJF cultivated with different patterns, processed using different methods and from different geographical regions. Moreover, the content of bioactive compounds in all LJF samples were determined to provide data support for explaining the intrinsic linkage between the chemical composition and anti-influenza virus activity of LJF.
3. Discussion
LJF has been employed in the treatment and prevention of epidemic diseases for thousands of years [
24]. A recent study confirmed that LJF decoction could suppress the replication of influenza virus [
4]. In that research, the anti-influenza virus activity of LJF was demonstrated by an inhibitory activity against NA. NA is a glycoside hydrolase that catalyzes the cleavage of sialic acid residues terminally linked to glycoproteins and glycolipids, thereby playing an important role in the release of progeny virions from the host cell to infect new cells [
25]. Clinical data revealed that NA inhibitors were effective against seasonal and pandemic influenza infections. To sum up, the inhibition of NA could be used to evaluate the anti-influenza virus activity.
The quality control of traditional Chinese medicine is the basis of its clinical efficacy, and the selection of bioactive compounds is crucial for the overall quality evaluation [
26]. In this study, multiple spectrum-effect relationship analysis methods were applied to comprehensively screen the bioactive compounds of LJF against influenza virus. Six bioactive compounds, including neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, 4,5-Di-
O-caffeoylquinic acid, sweroside and secoxyloganin, were screened out to evaluate the anti-influenza effect of LJF. Several antiviral efficacy studies showed that caffeoylquinic acids, chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid and 4,5-Di-
O-caffeoylquinic acid had a superior NA inhibitory activity, even higher than that of the positive control apigenin [
7,
27]. However, sweroside and secoxyloganin displayed a weak influenza NA inhibitory activity [
6,
7]. Interestingly, our results indicated that the content of secoxyloganin in the wLJF samples was much higher than that in the cLJF samples, and the NA inhibitory activity of the wLJF samples was significantly stronger than that of the cLJF samples (
Figure 4a and
Figure 6a). These results indicated that secoxyloganin had an important role in the anti-influenza effect of LJF, which could be the result of the synergistic effect of multiple compounds, such as caffeoylquinic acids and iridoid glycosides [
7]. Additionally, caffeoylquinic acids were also abundant in
Crataegus monogyna,
Eucalyptus globules,
Vaccinium angustifolium and coffee [
28], whereas these plants did not show obvious anti-influenza effect in previous reports [
29,
30,
31,
32]. This situation may be due to the absence of some synergistic components, perhaps iridoid glycosides, to assist caffeoylquinic acids in the resistance of influenza viruses.
The quality and anti-influenza activity of LJF was influenced by cultivation pattern, geographical origin and processing method. It was necessary to explore a strategy to evaluate the quality of different LJF samples. In this study, chemical pattern recognition models were established to assess the quality of LJF. There are two types of cultivation pattern for LJF, cultivated LJF which is usually grown on a large scale in planting bases and wild LJF, which is often distributed in brushwood, roadsides and village fences. Our study was the first to demonstrate the difference between cultivated and wild LJF samples from both perspectives of component accumulation and anti-influenza activity. Meanwhile, an LDA model was established to quickly and intuitively evaluate the quality of cultivated and wild LJF samples (
Figure 3a). In addition, the contents of bioactive compounds can be affected by the place of origin, because soil and climate conditions vary greatly with the geographical origin. In China, Henan, Hebei and Shandong provinces are the three main geographical origins of LJF. Pingyi County in Shandong Province is famous for its long cultivation history. Fengqiu County in Henan Province showed an advantage based on its large output and wide planting area. The LJF from Julu County in Hebei Province was relatively cheap and of high quality. As shown in
Figure 4b and
Figure 6b, geographical origins led to no impact on the anti-influenza effect of LJF, or on the content of chlorogenic acid and secoxyloganin. In accordance with previous studies, the content of chlorogenic acid from Shandong, Henan and Hebei was of inappreciable difference [
14,
33]. Although the content of the other four bioactive compounds was higher in Henan than Shandong, the total content of such four compounds from Henan (13.897 mg/g) was slightly higher than that from Shandong (12.037 mg/g). In addition, such a difference in content did not affect the NA inhibitory activity of LJF. Furthermore, the LDA models of geographical origins illustrated the samples were similar (
Figure 3b). These results confirmed the good and uniform quality of LJF samples from the main geographical origins, whereas the differences in quality of the LJF samples between the main origins and other origins remained to be investigated. As we all know, fresh LJF is extremely perishable due to its high moisture content, and therefore it must be processed immediately by drying after harvest [
15]. Hot-air drying and sun drying are two primary processing methods for LJF. No difference was observed in terms of anti-influenza activity between hot-air drying and sun drying (
Figure 4c). Furthermore, the content of chlorogenic acid, sweroside and 4,5-Di-
O-caffeoylquinic acid in samples processed by hot-air drying and sun drying were comparable (
Figure 6c). Moreover, the results were consistent with the reports indicating that there were no significant differences between these three compounds in hot-air-dried and sun-dried LJF samples [
14,
33]. The difference in total content of the six bioactive compounds in the LJF samples between hot-air drying (28.144 mg/g) and sun drying (25.460 mg/g) was small. Surprisingly, such a slight difference was displayed clearly by the LDA model (
Figure 3c). Even so, the comparable results above still indicated a great quality of cLJF processed by hot-air drying and sun drying as is commonly believed.
To the best of our knowledge, it was the first time the impact of quality-affecting factors on the anti-influenza virus activity of LJF was investigated. Meanwhile, this study demonstrated that the quality evaluation method based on clinical efficacy was promising over the methods concentrating on chemical profiles for traditional Chinese medicine. Additionally, our study revealed the intrinsic linkage between the bioactive compounds and the anti-influenza virus activity of LJF. Furthermore, the results above indicated that tiny content differences might hardly cause changes in the efficacy of LJF, indirectly proving the integrity and synergy of traditional Chinese medicine.
4. Materials and Methods
4.1. Chemicals, Reagents and Materials
Neochlorogenic acid, cryptochlorogenic acid and secoxyloganin reference standards at 98% purity were purchased from Shanghai Standard Technology Co., Ltd. (Shanghai, China). Chlorogenic acid (purity ≥ 98.3%), sweroside (purity ≥ 97.1%) and 4,5-Di-O-caffeoylquinic acid (purity ≥ 94.1%) were purchased from National Institutes for Food and Drug Control (Beijing, China). Acetonitrile (HPLC-MS grade) was acquired from Merck KGaA (Darmstadt, Germany). Formic acid (HPLC grade) was supplied by Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Milli-Q® water was purified in-house by a Milli-Q Academic ultrapure water system (Millipore, Milford, MA, USA). All other chemicals used in the study were of analytical grade.
Two types of LJF samples, including cultivated and wild LJF, were collected from late April to early May 2020. Cultivated LJF samples were collected from different geographical origins in China and processed by different processing methods. The detailed information of the LJF samples was listed in
Table 6. All samples were authenticated by Professor Ji Zhang at National Institute for Food and Drug Control, Beijing, China. The voucher specimens were deposited in the cold sample room, Shenzhen Institute for Drug Control, Shenzhen, China.
4.2. Preparation of Sample Solutions and Standard Solutions
The aqueous extract was obtained as described previously [
18]. Briefly, the dried LJF samples (6.00 g) were soaked in 20-fold volumes of water for 1 h and extracted twice for 1 h each time by reflux. After filtration, the combined reflux liquid of each sample was concentrated and dried to obtain the extract powder. The powder was stored in a desiccator and dissolved in 5% (
v/
v) acetonitrile aqueous solution to the concentration of 10 mg/mL for chromatographic analysis. All the sample solutions were filtered by 0.22 μm microporous membrane, and the filtrates served as the test solutions. For the NA inhibitor screening assay, each extract powder was dissolved in water to a suitable concentration.
Reference standards, including neochlorogenic acid (3.83 mg), chlorogenic acid (5.90 mg), cryptochlorogenic acid (3.50 mg), sweroside (1.89 mg), secoxyloganin (2.46 mg) and 4,5-Di-O-caffeoylquinic acid (2.68 mg) were accurately weighed and dissolved in 50% methanol to obtain the stock solutions at concentrations of 1.9150 mg/mL, 2.9500 mg/mL, 1.7500 mg/mL, 0.9450 mg/mL, 1.2300 mg/mL and 1.3400 mg/mL, respectively. The working standard solutions were prepared by mingling each stock solution and diluting the mixed solution with 50% methanol to gain a series of applicable concentrations. As for the NA inhibitor screening assay of pure substances, each reference standard was prepared by dissolving each compound in 50% methanol to gain the stock solutions at concentration of 2.1200 mg/mL, 2.0000 mg/mL, 2.0400 mg/mL, 2.0200 mg/mL, 1.9400 mg/mL and 2.0400 mg/mL, respectively.
4.3. Instrumentation and Chromatographic Conditions
The chemical composition information of LJF was obtained by an Ultimate 3000 UHPLC (Thermo Fisher Scientific, Waltham, MA, USA) which was equipped with a quaternary solvent delivery system, an autosampler, a column thermostat and a diode array detector. The separation was performed on a Poroshell 120 SB-C18 column (4.6 mm × 150 mm, 2.7 µm, Agilent, Sunnyvale, CA, USA). The column temperature was maintained at 15 °C and 0.1% formic acid (eluent A) and acetonitrile (eluent B) were used as eluents in the gradient mode. The gradient program at a flow rate of 0.9 mL/min was as follows: 0–5 min, 5–5% B; 5–10 min, 5–10% B; 10–15 min, 10–10% B; 15–25 min, 10–20% B; 25–40 min, 20–30% B. Ahead of the elution, the reservation for ten minutes of 5% B was to equilibrate the column for the consequent run. The injection volume was 5 µL, and the compounds of interest were monitored in 240 nm.
4.4. Similarity Analysis
All LJF samples were chemically sketched under the chromatographic conditions mentioned above. The chromatographic fingerprints of 71 batches of LJF were matched automatically by Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A, Chinese Pharmacopoeia Committee). The similarity values of all sample fingerprints to the generated reference fingerprint were calculated using the similarity evaluation system. Moreover, a 71 (samples) × 41 (peaks) data matrix was obtained for further analysis.
4.5. NA Inhibitor Screening Assay
The anti-influenza activity of LJF was evaluated for its NA inhibitory capacity in this study, which was assayed by a commercially available neuraminidase inhibitor screening kit (Beyotime Institute of Biotechnology Co., Ltd., Shanghai, China). According to the instructions of the manufacturer, 70 µL of buffer solution was added to each well of a 96-well plate, 10 µL of NA and 10 µL of sample solution were sequentially added to each well. For a complete reaction, it was shaken for 1 min and incubated for 2 min at 37 °C. Then, 10 µL of fluorescent substrate was added into the plate to make a total volume of 100 µL. The concoction was entirely vibrated for 1 min and incubated for 30 min at 37 °C before detection. The fluorescence intensity (FI) was measured with an excitation wavelength of 322 nm and emission wavelength of 450 nm by a Thermo Scientific Microplate Reader (Thermo Fisher Scientific, Waltham, MA, USA). The inhibition rate of each sample was computed by the following formula: percent inhibition (%) = (FINA − FIsample)/FINA × 100%, where FINA is the FI of the control (without inhibitors) and FIsample is the FI of sample solutions.
4.6. Spectrum-Effect Correlation Analysis
In this paper, OPLS, Pearson correlation analysis and GRA were applied to investigate the correlation between chromatographic peaks and the anti-influenza activity. OPLS was performed by SIMCA 14.0 version (Umetrics AB, Umea, Sweden), and the Pearson correlation analysis and GRA were conducted by online software SPSSAU 20.0 (retrieved from
https://www.spssau.com, accessed on 14 June 2022). The areas of 41 chromatographic peaks were set as the X variables, and the results of the NA inhibitor screening assay were set as the Y variables, then the X–Y data set was imported into the software tool for analysis.
OPLS is a regression modeling method for multiple dependent variables to multiple independent variables [
34]. It is a variant of PLS which utilizes orthogonal signal correction to maximize the explained covariance between X and Y on the first latent variable [
35]. The Y-related profile plot and VIP plot were ulteriorly generated to select the main active compounds, according to the coefficients and VIP values [
36,
37].
Pearson correlation analysis is typically used for jointly normally distributed data, aiming to examine the degree of linearity of the relationship between variables [
38,
39]. To facilitate interpretation, the Pearson correlation coefficient, which is ordinarily abbreviated as “
r”, is commonly used as a dimensionless measure of the covariance ranging from −1 to +1 [
21]. It is conventionally known that an absolute value of
r (|
r|) < 0.1 indicates a negligible relationship, and |
r| > 0.9 indicates a very strong relationship.
GRA, which is also called “grey correlation degree”, is generally applied to assess the similarity of geometric curves as a means of determining the relationship between samples and tested objects [
40,
41,
42]. For analyzing herbal medicine fingerprint data, GRA is an optimal means of selecting the best alternative based on the GRG value, which is always distributed between 0 and 1 [
41]. The higher the GRG, the more significant the influence of the sequence to be compared to the reference sequence.
4.7. Quality Evaluation of Lonicerae Japonicae Flos by Chemical Pattern Recognition
A total of 71 batches of samples were partitioned into 47 batches for the training set and 24 batches for the testing set. Moreover, PCA was used to identify outliers using SIMCA 14.0 version (Umetrics AB, Umea, Sweden) [
43]. Before the modeling assessment, an autoscaling pretreatment was carried out on the data matrix for the elimination of variables’ dimensional influence. With spectrum-effect correlation analysis, the chromatographic peaks related to anti-influenza virus activity were obtained. Considering only the most relative peaks, LDA was used to create classification models by discriminant functions for given groups by means of discriminatory variables, which was performed by SPSS 22.0 software (IBM, Chicago, IL, USA). Then, these peaks were integrated as bioactive variables, applied to develop classification models of the LJF samples according to the quality-affecting factors using LDA. Thus, the quality of LJF was comprehensively evaluated from both chemical composition and efficacy by combining LDA classification models with NA inhibitory activity comparative analysis. In addition, model performance was measured in terms of accuracy, precision, sensitivity and F-score, the values of which close or equal to 1.00 indicate a good discriminative property [
44,
45].
4.8. UPLC/Q-TOF/MS Analysis
The UPLC/Q-TOF/MS analysis was performed on an ExionLCTM AD system connected with X500R QTOF (AB SCIEX, Foster City, CA, USA). Electrospray ionization mass spectra were acquired in negative ion mode by scanning over the range of 100–1500 Da for MS and 50–1500 Da for MS/MS. The optimized MS conditions were as follows: nebulizer gas (gas 1), 50 psi; heater gas (gas 2), 50 psi; curtain gas, 35 psi; ion spray voltage, 5500 V; ion source temperature, 550 °C; declustering potential, −80 V; collision energy, −35 V; CE spread, 15 V. The UPLC/Q-TOF/MS data was processed by SCIEX OS software.
4.9. Statistical Analysis
All experiments were performed in triplicate, and the results were expressed as the mean ± SD. The statistical analysis was done by an unpaired t-test and a one-way analysis of variance (ANOVA) followed by Tukey’s honestly significant difference, using GraphPad Prism 9.0 software (GraphPad Software Inc., San Diego, CA, USA). p < 0.05 was considered to be significant.