Identification of Anti‐Inflammatory Active Components in Morinda officinalis via Spectroscopic Effect Relationship Analysis and NF‐κB Pathway Inhibition

ABSTRACT

Morindae Officinalis Radix (MOR), the dried root of Morinda officinalis How, contains abundant saccharides and is used clinically after different processing procedures. However, the anti‐inflammatory material basis of MOR and the chemical consequences of Lycium barbarum processing remain insufficiently defined. In this study, high‐performance liquid chromatography coupled with evaporative light scattering detection (HPLC‐ELSD) was developed for fingerprint analysis of 15 batches of raw MOR and the corresponding L. barbarum ‐processed MOR (LB‐MOR). Seventeen common peaks were resolved, and 11 saccharides were assigned by comparison with reference standards. Anti‐inflammatory activity was evaluated in LPS‐stimulated RAW 264.7 macrophages by quantifying nitric oxide (NO), TNF‐α, IL‐1β, and IL‐6. Grey relational analysis and partial least squares regression linked the HPLC fingerprints with bioactivity and identified glucose, sucrose, kestose, and nystose as the principal activity‐associated saccharides. Subsequent validation showed that these saccharides inhibited inflammatory mediator release, with kestose and nystose showing the strongest effects. Western blotting further indicated that kestose and nystose restored IκBα expression and decreased COX‐2 expression and the p‐p65/p65 ratio, consistent with suppression of sustained NF‐κB signaling. This separation‐based fingerprint and spectrum‐effect workflow identify anti‐inflammatory oligosaccharides in MOR and provides candidate quality markers for raw and processed MOR products.

1. Introduction

Natural products, particularly bioactive constituents derived from traditional Chinese medicine (TCM), represent an important source of pharmacologically active compounds with anti‐inflammatory and antitumor potential. For instance, matrine has recently been reported to suppress liver cancer progression by targeting Annexin A2, restoring tumor‐associated antigen presentation, and enhancing CD8⁺ T‐cell‐mediated antitumor immunity (Tang et al. 2025). Wogonoside has been shown to attenuate inflammation‐associated colorectal tumorigenesis by inhibiting NF‐κB activation via the PI3K/Akt pathway (Sun et al. 2016), whereas saikosaponins a and d exert anti‐inflammatory effects by suppressing NF‐κB activation and reducing the production of inflammatory mediators in LPS‐stimulated RAW 264.7 macrophages (Lu et al. 2012). Collectively, these findings underscore the pharmacological potential of TCM‐derived natural products and provide a broader context for identifying anti‐inflammatory bioactive constituents from medicinal herbs.

Within this context, Morindae officinalis Radix (MOR; Ba Ji Tian in Chinese), the dried root of Morinda officinalis How, is officially recorded in the Pharmacopoeia of the People's Republic of China (Pharmacopoeia of The People's Republic of China 2025). MOR is widely used in TCM and other herbal medicine systems throughout East Asia. Modern pharmacological studies have reported a broad range of biological activities for MOR and its derived fractions, including anti‐inflammatory, anti‐osteoporotic, antidepressant, anti‐fatigue, and neuroprotective effects (Cai et al. 2021; Zhang et al. 2018).

The phytochemical profile of MOR is complex and comprises multiple classes of constituents, including anthraquinones, iridoid glycosides, polysaccharides, and oligosaccharides (Yue et al. 2024). Among these constituents, oligosaccharides are particularly abundant and have attracted increasing attention because of their reported immunomodulatory, antidepressant, anti‐inflammatory, and anti‐aging activities (Lai et al. 2023; Shen et al. 2024; Wu et al. 2024). Nevertheless, the specific saccharides that contribute to the anti‐inflammatory activity of MOR and their potential utility as quality markers remain insufficiently characterized.

Processing is an integral component of the clinical application and quality control of Chinese materia medica. The Chinese Pharmacopoeia includes several processed forms of MOR (Pharmacopoeia of The People's Republic of China 2025). L. barbarum fruit is also a widely used medicinal and edible material rich in saccharides and other bioactive constituents and has traditionally been used as a tonic ingredient (Yang et al. 2024). Accordingly, processing MOR with an aqueous extract of L. barbarum is expected to alter its saccharide profile. However, the chemical changes induced by this processing procedure and the associated bioactive constituents have not yet been systematically characterized.

Chemical fingerprinting provides an effective strategy for evaluating the overall chemical composition and batch‐to‐batch consistency of herbal medicines. When chromatographic fingerprint data are integrated with bioactivity measurements, spectrum‐effect relationship analysis can facilitate the identification of constituents associated with pharmacological effects without requiring the prior isolation of all individual compounds (Zhang et al. 2024; Zhong et al. 2024). Grey relational analysis (GRA) and partial least squares regression (PLSR) are particularly useful for modeling multivariate spectrum‐effect relationships (Fu et al. 2025; Xiao et al. 2022). For MOR, high‐performance liquid chromatography coupled with evaporative light‐scattering detection (HPLC‐ELSD) is particularly suitable for establishing saccharide‐rich fingerprints because many sugars and fructooligosaccharides lack strong ultraviolet absorption.

Macrophages are key effector cells involved in inflammatory responses. Upon activation, they produce and release a range of pro‐inflammatory mediators, including nitric oxide (NO), tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), and interleukin‐6 (IL‐6), thereby amplifying inflammatory responses (Fang et al. 2021; Hannoodee and Nasuruddin 2024; Stone et al. 2024; Tian et al. 2021). LPS‐stimulated RAW 264.7 macrophages constitute a widely used in vitro model for screening the anti‐inflammatory activity of natural products and investigating their underlying molecular mechanisms (Facchin et al. 2022).

Among the signaling pathways involved in inflammation, the NF‐κB pathway is a central regulator of inflammatory mediator and cytokine production (Chen et al. 2021; Lin et al. 2025; Veerasubramanian et al. 2024; Yu et al. 2020; Zhang et al. 2021). Under resting conditions, NF‐κB is sequestered in the cytoplasm through its interaction with inhibitory IκB proteins. Upon stimulation with LPS or other pro‐inflammatory stimuli, IκBα undergoes phosphorylation and subsequent degradation, thereby facilitating p65 phosphorylation and NF‐κB activation. Activated NF‐κB subsequently promotes the expression of pro‐inflammatory genes, including those encoding TNF‐α, IL‐1β, IL‐6, and COX‐2, and contributes to enhance NO production.

In the present study, HPLC‐ELSD fingerprints were established for 15 batches of raw MOR and the corresponding L. barbarum ‐processed MOR (LB‐MOR) samples. Their anti‐inflammatory activities were evaluated by measuring the levels of NO, TNF‐α, IL‐1β, and IL‐6 in LPS‐stimulated RAW 264.7 macrophages. GRA and PLSR were subsequently applied to correlate chromatographic peaks with anti‐inflammatory activity and to screen for candidate bioactive constituents.

The candidate active saccharides predicted by the spectrum‐effect relationship analysis were subsequently validated experimentally, and the effects of the two leading candidates, kestose and nystose, on NF‐κB signaling were further investigated by Western blotting. By integrating chromatographic fingerprinting, spectrum‐effect relationship analysis, and biological validation, this study aimed to establish a separation science‐based framework for the quality evaluation of MOR and LB‐MOR and to identify saccharide constituents associated with their anti‐inflammatory activity.

2. Materials and Methods

2.1. Materials and Reagents

Fifteen batches of MOR samples were collected, with samples S1–S11 originating from Guangdong Province and samples S12–S15 from Guangxi Province, China. All samples were authenticated by Professor Li Feng of Liaoning University of Traditional Chinese Medicine as the dried roots of Morinda officinalis How, corresponding to the medicinal material Morindae Officinalis Radix (MOR). The fibrous roots were removed, and the samples were dried at 50°C in a constant‐temperature oven. A voucher specimen of M. officinalis (Voucher ID: 2024.06.06) was deposited in the Herbarium of Liaoning University of Traditional Chinese Medicine. All procedures involving plant materials were conducted in accordance with relevant institutional, national, and international guidelines and regulations.

Reference standards of D‐glucose (Glc; Cat. No. PS020418), D‐fructose (Fru; Cat. No. PS020484), sucrose (Suc; Cat. No. PS020086), kestose (GF₂; Cat. No. PS011429), nystose (GF₃; Cat. No. PS011428), sucrose fructooligosaccharide pentasaccharide (GF₄; Cat. No. PS011430), sucrose fructooligosaccharide hexasaccharide (GF₅; Cat. No. DSTDZ007701), sucrose fructooligosaccharide heptasaccharide (GF₆; Cat. No. DSTDZ008801), sucrose fructooligosaccharide octasaccharide (GF₇; Cat. No. DST211106‐089), sucrose fructooligosaccharide nonasaccharide (GF₈; Cat. No. DSTDZ016801), and sucrose fructooligosaccharide decasaccharide (GF₉; Cat. No. DSTDZ009101) were obtained from Chengdu Pus Bio‐technology Co. Ltd. (Chengdu, China), each with a purity of ≥ 98%. Ultrapure water and other analytical‐grade reagents were used throughout the study. Acetonitrile and formic acid of chromatographic grade were purchased from Merck (Germany).

RAW 264.7 murine macrophages were obtained from the Cell Bank of the Chinese Academy of Sciences (Kunming, China). Fetal bovine serum (FBS) was purchased from GIBCO (New York, NY, USA). Lipopolysaccharide (LPS), phosphate‐buffered saline (PBS), dexamethasone, and high‐glucose Dulbecco's Modified Eagle's Medium (DMEM) were obtained from Beijing Solabao Technology Co. Ltd. (Beijing, China). The Cell Counting Kit‐8 (CCK‐8) and penicillin–streptomycin solution were obtained from Seville Biotechnology Co. Ltd. (Seville, Spain). ELISA kits for IL‐1β, IL‐6, NO, and TNF‐α were purchased from Shanghai Kexing Trading Co. Ltd. (Shanghai, China). Primary antibodies against β‐actin, COX‐2, IκBα, p65, and phosphorylated p65 (Ser536), together with the corresponding secondary antibody, were obtained from Wuhan Sanying Biotechnology Co. Ltd. (Wuhan, China).

2.2. Sample Preparation

2.2.1. Preparation of L. barbarum ‐Processed MOR (LB‐MOR)

For preparation of the L. barbarum aqueous extract, 6 g of sliced L. barbarum fruit was extracted with water three times, with each extraction lasting 1.5 h. The resulting extracts were combined and concentrated to a final volume of 150 mL.

For preparation of LB‐MOR, 100 g of MOR was immersed in the prepared L. barbarum aqueous extract for 3.0 h and subsequently steamed for 2.5 h. The processed material was then oven‐dried at 60°C to obtain LB‐MOR. The resulting LB‐MOR segments were flat and cylindrical, with a dark gray to dark brown surface characterized by transverse fissures and longitudinal striations; dark purple or purple patches were occasionally observed.

2.2.2. Preparation of Reference Standard Solution

Appropriate amounts of Glc, Fru, Suc, GF₂, GF₃, GF₄, GF₅, GF₆, GF₇, GF₈, and GF₉ reference standards were accurately weighed and dissolved in 50% methanol to prepare individual stock solutions at concentrations of 2.424, 3.456, 2.415, 1.035, 5.180, 3.155, 3.065, 2.653, 2.246, 1.642, and 2.524 mg/mL, respectively. Equal volumes of the individual stock solutions were combined to prepare a mixed standard solution. A series of calibration solutions at different concentrations was subsequently prepared to cover the expected concentration ranges of the target analytes in MOR samples and to ensure that all measurements fell within the linear response ranges of the analytical method.

2.2.3. Preparation of the Test Solution

Dried powdered MOR and LB‐MOR samples (0.5 g, passed through a 60‐mesh sieve) were separately extracted with 30 mL of ethyl acetate by ultrasonication for 30 min. After filtration, the ethyl acetate filtrate was discarded to remove non‐polar interfering constituents. The residue was subsequently extracted with 50 mL of 70% aqueous ethanol by ultrasonication for 30 min, and the filtrate was collected. After concentration under reduced pressure, the residue was reconstituted in water and quantitatively transferred to a 10 mL volumetric flask. The solution was brought to volume with water and filtered through a 0.22 μm membrane before HPLC‐ELSD analysis. This cleanup procedure was not applied to the reference standard solutions.

2.2.4. Preparation of Extracts for Cell‐Based Assays

For the anti‐inflammatory assays, 5.0 g of each MOR and LB‐MOR sample from the 15 batches was separately placed in a ground‐glass Erlenmeyer flask. Ethyl acetate (300 mL) was added, and the samples were extracted by ultrasonication for 6 h. After filtration, the ethyl acetate filtrate was discarded, and the residue was dried to remove residual solvent. The dried residue was subsequently extracted with 500 mL of 70% ethanol by ultrasonication for 6 h. After filtration, the ethanol extract was concentrated under reduced pressure using a rotary evaporator to remove ethanol. The concentrated extract was then freeze‐dried, and the resulting lyophilized powder was dissolved in culture medium at the required concentrations for subsequent cell‐based experiments.

2.3. HPLC‐ELSD Conditions

HPLC‐ELSD analysis was performed using an ELSD‐LT III detector coupled to a Nexera LC‐40 liquid chromatography system. Chromatographic separation was achieved on an XBridge Amide column (3.5 μm, 4.6 mm × 250 mm) using water (A) and acetonitrile (B) as the mobile phases. The gradient elution program was as follows: 0–5 min, 22%–24% A; 5–8 min, 24%–33% A; 8–15 min, 33%–41% A; 15–25 min, 41%–46% A; 25–30 min, 46%–52% A; and 30.01–36 min, 22% A. The flow rate was 1.0 mL/min, and the injection volume was 5 μL. The ELSD gas pressure was maintained at 3.5 bar. The drift‐tube and column temperatures were maintained at 45°C and 30°C, respectively.

2.4. Method Validation

The performance of the HPLC‐ELSD method was evaluated in terms of instrumental precision, repeatability, and sample stability. Instrumental precision was assessed by six consecutive injections of the same sample solution. Repeatability was evaluated using six independently prepared MOR sample solutions processed according to the procedure described in Section 2.2.3. Sample stability was assessed by analyzing aliquots of the same sample solution maintained at room temperature at 0, 4, 8, 12, 24, and 48 h. The relative standard deviations (RSDs) of the relative peak areas of the common chromatographic peaks were calculated to evaluate the reliability of the method for comparative fingerprint analysis.

2.5. Anti‐Inflammatory Activity of MOR and LB‐MOR Samples

2.5.1. Cell Culture

RAW 264.7 cells were cultured in high‐glucose DMEM supplemented with 10% FBS and 1% penicillin–streptomycin at 37°C in a humidified atmosphere containing 5% CO₂. RAW 264.7 macrophages were selected as the in vitro inflammatory model because of their well‐established and reproducible response to LPS stimulation and their widespread use in anti‐inflammatory screening and NF‐κB signaling studies. Cells between passages 10 and 20 were used throughout the experiments to minimize passage‐related variability.

2.5.2. Sample Preparation

A stock solution of the MOR extract was prepared by dissolving the freeze‐dried powder of sample S1, obtained as described in Section 2.2.4, in complete culture medium at a concentration equivalent to 6.4 mg/mL of crude drug. The solution was filtered through a 0.22 μm membrane before use.

2.5.3. Measurement of RAW 264.7 Cell Viability

RAW 264.7 cells in the logarithmic growth phase were harvested and adjusted to a density of 1.5 × 10⁵–2.0 × 10⁵ cells/mL. Cell suspension (200 μL) was added to each well of a 96‐well plate, whereas blank wells contained 200 μL of complete culture medium without cells. After incubation at 37°C with 5% CO₂ for 20 h, the culture medium was removed. Cells in the treatment groups were then incubated with complete medium containing MOR extract at concentrations of 6.4, 3.2, 1.6, 0.8, 0.4, 0.2, 0.1, or 0.05 mg/mL. Blank and normal control wells received complete culture medium without MOR extract. Six replicate wells were included for each condition.

After 24 h of treatment, the culture medium was removed and 200 μL of CCK‐8 reagent was added to each well. The plates were incubated for an additional 3 h at 37°C with 5% CO₂, after which absorbance was measured at 450 nm using a microplate reader. Cell viability was calculated relative to the normal control group. The experiment was independently repeated at least three times using separately cultured cell batches.

2.5.4. Determination of NO, TNF‐α, IL‐1β, and IL‐6 Levels

RAW 264.7 cells were seeded into 96‐well plates and incubated for 20 h. Cells assigned to the LPS model and treatment groups were then stimulated with complete culture medium containing 2 μg/mL LPS, whereas the blank and normal control groups received complete culture medium without LPS. After 10 h of LPS stimulation, cell morphology was examined microscopically to verify establishment of the inflammatory phenotype. This pre‐stimulation period was used to establish a sustained inflammatory state before administration of the test samples.

The medium was subsequently removed, and cells in the treatment groups were incubated with complete culture medium containing MOR extract at concentrations of 0.05, 0.1, or 0.2 mg/mL. Three replicate wells were used for each concentration. After 12 h of treatment, the culture supernatants were collected, and the levels of NO, TNF‐α, IL‐1β, and IL‐6 were determined using the corresponding assay kits according to the manufacturers' instructions.

2.6. Spectrum‐Effect Relationship Analysis

2.6.1. Grey Relational Analysis

Grey relational analysis (GRA) was performed to evaluate the associations between HPLC fingerprint peaks and anti‐inflammatory activity. The areas of the common chromatographic peaks obtained from MOR and LB‐MOR samples and the corresponding levels of NO, TNF‐α, IL‐1β, and IL‐6 in RAW 264.7 cells were analyzed using the SPSSPAU online platform. Cytokine data were designated as the reference sequences, whereas chromatographic peak areas were used as the comparison sequences. Before analysis, the data were normalized using the mean‐value method to eliminate dimensional differences. Higher grey relational degrees indicated stronger associations between individual chromatographic peaks and the measured biological responses.

2.6.2. PLSR Analysis

Partial least squares regression (PLSR) was used to further evaluate the relationships between chromatographic variables and anti‐inflammatory activity while accounting for multicollinearity among the chromatographic peaks. The peak areas of the 17 common chromatographic peaks from MOR and LB‐MOR samples were used as the independent variables (X), whereas the corresponding levels of NO, TNF‐α, IL‐1β, and IL‐6 were used as the dependent variables (Y). PLSR modeling was performed using SIMCA‐P 14.1. Regression coefficients and variable importance in projection (VIP) scores were used to estimate the relative contribution of individual peaks. Model performance was evaluated using R²X, R²Y, and Q² values, together with a 200‐permutation test and CV‐ANOVA.

2.7. Biological Validation Methods

2.7.1. Validation of the Anti‐Inflammatory Activity of Candidate Saccharide

The anti‐inflammatory activities of the four candidate saccharides identified by the spectrum‐effect relationship analysis—glucose, sucrose, kestose (GF₂), and nystose (GF₃)—were further evaluated in LPS‐stimulated RAW 264.7 macrophages. Cells were stimulated with 2 μg/mL LPS for 10 h and subsequently treated with each saccharide at concentrations of 50, 100, or 200 μM for 12 h. Untreated control and LPS model groups were included. After treatment, culture supernatants were collected, and the levels of NO, TNF‐α, IL‐1β, and IL‐6 were measured using the corresponding assay kits according to the manufacturers' instructions.

2.7.2. Western Blot Analysis

The effects of kestose and nystose on NF‐κB signaling were examined by Western blot analysis. RAW 264.7 cells were seeded in 6‐well plates at a density of 5 × 10⁵ cells per well and cultured overnight until approximately 80% confluence. Cells were stimulated with LPS as described in Section 2.5.4 and subsequently treated with kestose or nystose at 50 μM.

After treatment, cells were placed on ice and lysed with pre‐cooled RIPA lysis buffer supplemented with 1 × protease inhibitor cocktail and 1 × phosphatase inhibitor cocktail. Whole‐cell lysates were collected, and total protein concentrations were determined using a BCA protein assay kit. Equal amounts of protein were separated by 10% SDS‐PAGE and transferred onto PVDF membranes. After blocking with 5% skim milk or bovine serum albumin for 3 h at room temperature, the membranes were incubated overnight at 4°C with primary antibodies against COX‐2 (Cat. No. 00150356, 1:1000), IκBα (Cat. No. 00150345, 1:10,000), p65 (Cat. No. 00160675, 1:1000), and phosphorylated p65 (Ser536; Cat. No. 23005077, 1:5000).

The membranes were washed three times with 1 × TBST for 10 min each and subsequently incubated with horseradish peroxidase‐conjugated goat anti‐rabbit IgG secondary antibody (1:20,000) for 1 h at room temperature. After three additional washes with TBST, protein bands were visualized using an enhanced chemiluminescence substrate. Band intensities were quantified using ImageJ software, with β‐actin serving as the loading control.

2.8. Statistical Analysis

Quantitative data are presented as the mean ± standard deviation (SD) from at least three independent biological experiments performed on different days using independently cultured cell batches. For experiments involving multiple groups, statistical comparisons were performed using one‐way analysis of variance (ANOVA), followed by Dunnett's post hoc test for comparisons with a single control group or Tukey's post hoc test for multiple pairwise comparisons, as appropriate. Comparisons between two groups were performed using an unpaired two‐tailed Student's t‐test. A p value < 0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism version 9.0 (GraphPad Software, San Diego, California, USA).

3. Results

3.1. HPLC Fingerprint Analysis

3.1.1. Optimization of HPLC‐ELSD Conditions

The HPLC‐ELSD conditions were systematically optimized to achieve satisfactory chromatographic separation. Comparison of different mobile‐phase systems showed that the water–acetonitrile system provided better overall resolution than the 0.1% phosphoric acid aqueous system. Among the drift‐tube temperatures evaluated (40°C–55°C), 45°C provided the optimal chromatographic response and peak separation.

Three chromatographic columns were compared: Ultimate Amide (4.6 × 250 mm, 3 μm), NH2P‐50 4E (4.6 × 150 mm, 5 μm), and XBridge Amide (4.6 × 250 mm, 3.5 μm). The XBridge Amide column was selected because it provided superior overall resolution and stable separation of polar compounds, particularly glucose and sucrose. These optimized chromatographic conditions provided satisfactory separation of the major saccharide components in MOR samples.

3.1.2. Method Validation

The HPLC‐ELSD method was evaluated in terms of instrumental precision, repeatability, and sample stability. Instrumental precision was assessed by six consecutive injections of the same test solution, with relative standard deviations (RSDs) of the relative peak areas below 1.09% for the evaluated characteristic peaks. Repeatability was assessed using six independently prepared MOR sample solutions, for which the RSDs of the relative peak areas of the 17 common peaks were also below 1.09%. The RSDs obtained in the sample stability test were below 1.68% over the evaluated storage period. Collectively, these results demonstrate that the established HPLC‐ELSD method provides adequate precision, repeatability, and stability for comparative fingerprint analysis of MOR and LB‐MOR (Table 1).

TABLE 1.

HPLC‐ELSD method validation parameters.

Analytic	Linear range (mg/mL)	Calibration curve	R 2	Repeatability RSD (%)	Precision (RSD, %)	Sensitivity	LOD (μg/mL)	LOQ (μg/mL)
Glc	2.42~12.12	Y = 1125.6X + 28.5	0.9995	0.35	0.42	0.65	0.21	0.66
Fru	3.46~17.28	Y = 1086.3X + 32.1	0.9996	0.42	0.48	0.72	0.25	0.75
Suc	2.42~12.08	Y = 1058.7X + 25.3	0.9996	0.42	0.39	0.61	0.20	0.60
GF2	1.04~5.18	Y = 986.5X + 18.7	0.9995	0.52	0.58	0.85	0.12	0.38
GF3	5.18~25.90	Y = 1256.8X + 45.2	0.9994	0.28	0.37	0.57	0.32	0.97
GF4	3.16~15.78	Y = 925.4X + 22.6	0.9998	0.74	0.58	0.84	0.19	0.56
GF5	3.07~15.33	Y = 896.2X + 20.8	0.9997	0.75	0.74	0.57	0.17	0.51
GF6	2.65~13.27	Y = 852.3X + 19.5	0.9996	0.85	0.82	0.68	0.16	0.50
GF7	2.25~11.23	Y = 815.7X + 17.3	0.9998	0.75	0.86	0.69	0.15	0.47
GF8	1.64~8.21	Y = 786.9X + 15.2	0.9995	1.09	1.09	1.68	0.12	0.36
GF9	2.52~12.62	Y = 832.5X + 18.9	0.9997	1.01	0.99	1.53	0.31	0.93

3.1.3. Establishment and Evaluation of HPLC Fingerprints

Thirty samples, comprising 15 batches of raw MOR (S1–S15) and their corresponding LB‐MOR products (P1–P15), were prepared according to Section 2.2.3 and analyzed under the chromatographic conditions described in Section 2.3. Chromatographic data were acquired in CDF format and processed using the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2012A). A reference fingerprint was generated from the chromatograms of the 30 samples using the median method (Figure 1).

FIGURE 1.

Comparative HPLC fingerprint profiles of raw and processed Morindae officinalis Radix (MOR). (A) Overlaid chromatograms of 15 batches of raw MOR. (B) Overlaid chromatograms of 15 batches of processed MOR (LB‐MOR). (C) The generated reference fingerprint (similarity‐mean chromatogram).

Seventeen common chromatographic peaks were identified, of which 11 components were assigned by comparison of their retention times with those of authentic reference standards. Similarity analysis showed values ranging from 0.980 to 1.000 for all samples except S14 and P14 (Table 2), indicating a generally high degree of chromatographic similarity among the samples.

TABLE 2.

Similarity evaluation results of 15 batches of MOR and LB‐MOR.

Sample number	Similarity	Sample number	Similarity	Sample number	Similarity	Sample number	Similarity	Sample number	Similarity	Sample number	Similarity
S1	0.998	S6	0.995	S11	0.998	P1	0.995	P6	0.992	P11	0.99
S2	0.996	S7	0.998	S12	0.995	P2	0.988	P7	0.990	P12	0.996
S3	0.997	S8	0.992	S13	0.990	P3	0.986	P8	0.996	P13	0.996
S4	0.997	S9	0.994	S14	0.652	P4	0.983	P9	0.989	P14	0.585
S5	0.984	S10	0.999	S15	0.996	P5	0.980	P10	0.989	P15	0.996

Despite the high overall similarity, distinct changes were observed in specific chromatographic regions after processing. The relative responses of the peak cluster eluting at approximately 9.0–9.5 min were markedly increased in LB‐MOR, whereas those of peaks eluting at approximately 23–26 min were decreased compared with raw MOR. These findings indicate that L. barbarum processing induced reproducible changes in specific regions of the saccharide chromatographic profile while largely preserving the overall fingerprint characteristics.

3.2. Effects of MOR and LB‐MOR on LPS‐Induced Inflammatory Model in RAW264.7 Cells

3.2.1. Effects on RAW264.7 Cell Viability

Cell viability was evaluated using the CCK‐8 assay and calculated as follows: Relative cell viability = (A_treatment − A_blank) / (A_normal − A_blank) × 100%.

Based on the viability results, concentrations of 0.05–0.2 mg/mL were selected for subsequent anti‐inflammatory experiments. As shown in Table 3, cell viability remained above 100% within this concentration range, indicating no detectable cytotoxicity under the experimental conditions. In contrast, reductions in cell viability were observed at some concentrations above 0.2 mg/mL. Accordingly, 0.05–0.2 mg/mL was considered an appropriate concentration range for subsequent evaluation of anti‐inflammatory activity.

TABLE 3.

Effects of MOR at different concentrations on cell viability in LPS‐induced RAW264.7 macrophages (²²¹²x ± s, n = 6). Cell viability was assessed using the CCK‐8 assay. ^# p < 0.05, ^## p < 0.01, ^### p < 0.001 versus the Blank group; *p < 0.05, **p < 0.01, ***p < 0.001 versus the Control group.

Group	Mass concentration/(mg·mL−1)	Cell survival rate/%
Blank	—	0.00 ± 0.65
Control	—	100.07 ± 0.14 ##
MOR and LB‐MOR	6.4	99.42 ± 3.19
	3.2	93.77 ± 1.81*
	1.6	97.06 ± 1.92
	0.8	84.23 ± 1.85**
	0.4	97.51 ± 0.87
	0.2	136.19 ± 0.42**
	0.1	156.36 ± 1.68**
	0.05	148.42 ± 1.79**

3.2.2. Effects of Different Concentrations on Inflammatory Markers in RAW264.7 Cells

The effects of MOR extract on the production of NO, TNF‐α, IL‐1β, and IL‐6 were evaluated in LPS‐stimulated RAW 264.7 macrophages. LPS stimulation markedly increased the levels of all four inflammatory mediators compared with the normal control group (p < 0.01), confirming successful establishment of the inflammatory model. Treatment with MOR extract at concentrations of 0.05–0.2 mg/mL significantly reduced the levels of NO, TNF‐α, IL‐1β, and IL‐6 compared with the LPS group, with a generally concentration‐dependent inhibitory trend (Table 4).

TABLE 4.

Effects of MOR extract on inflammatory mediators in LPS‐stimulated RAW 264.7 cells.RAW 264.7 cells were treated with indicated concentrations of MOR extract after LPS (2 μg/mL) stimulation. Levels of NO, TNF‐α, IL‐1β, and IL‐6 in the culture supernatant were quantified using ELISA kits (²²¹²x ± s, n = 3). ^# p < 0.05, ^## p < 0.01 versus the Control group; *p < 0.05, **p < 0.01 versus the LPS group.

Group	Mass concentration/(mg·mL−1)	NO (μmol·L−1)	TNF‐α (pg/mL)	IL‐1β (pg/mL)	IL‐6 (pg/mL)
Control	—	2.87 ± 0.19	91.55 ± 1.64	5.34 ± 0.01	21.31 ± 0.30
LPS	—	19.49 ± 0.99##	1246.41 ± 0.27##	188.24 ± 1.01##	272.71 ± 1.71##
MOR and LB‐MOR	0.05	15.86 ± 0.27**	781.96 ± 0.60**	159.57 ± 0.58**	194.80 ± 1.87**
	0.1	13.16 ± 0.27**	736.04 ± 1.71**	151.41 ± 1.15**	182.56 ± 0.45**
	0.2	12.54 ± 0.14**	635.09 ± 1.59**	142.01 ± 1.73**	183.00 ± 1.29**

Based on these findings, 0.2 mg/mL was selected for subsequent spectrum‐effect relationship analysis because it produced pronounced inhibition of inflammatory mediator production while maintaining high cell viability. This concentration was therefore used to evaluate the 15 batches of MOR and their corresponding LB‐MOR samples.

3.2.3. In Vitro Anti‐Inflammatory Activity of MOR and LB‐MOR

Fifteen batches each of raw MOR and the corresponding LB‐MOR samples were evaluated in LPS‐stimulated RAW 264.7 macrophages. As shown in Figure 2, all tested samples significantly reduced the production of NO, TNF‐α, IL‐1β, and IL‐6, although the magnitude of inhibition varied among batches. Overall, LB‐MOR samples showed greater inhibitory effects than their corresponding raw MOR samples, indicating that L. barbarum processing was associated with enhanced anti‐inflammatory activity under the experimental conditions. For comparative analysis, the levels of inflammatory mediators in the LPS model group were normalized to 100%.

FIGURE 2.

Inhibitory effects of MOR and LB‐MOR extract (0.2 mg/mL) on NO, TNF‐α, IL‐1β, and IL‐6 production in LPS‐stimulated (2 μg/mL) RAW 264.7 cells. S: MOR, P: LB‐MO, DEX: dexamethasone (positive control). ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05.

3.3. Spectrum‐Effect Relationship Analysis

3.3.1. Grey Relational Analysis

GRA was performed to evaluate the associations between the 17 common HPLC fingerprint peaks and the anti‐inflammatory responses of MOR and LB‐MOR samples. All common peaks exhibited grey relational degrees greater than 0.5 (Table 5), suggesting that multiple chromatographic components may contribute to the observed anti‐inflammatory activity.

TABLE 5.

Correlation between common peak areas of 15 batches of MOR and LB‐MOR with anti‐inflammatory efficacy.

Common peak	Correlation degree	Associative order	Common peak	Correlation degree	Associative order
F1	0.852	2	F10	0.811	7
F2	0.789	9	F11	0.801	8
F3	0.750	15	F12	0.787	10
F4	0.750	14	F13	0.775	11
F5	0.868	1	F14	0.763	12
F6	0.848	3	F15	0.753	13
F7	0.843	4	F16	0.742	16
F8	0.835	5	F17	0.731	17
F9	0.823	6

Using a distinguishing coefficient of 0.8, eight saccharide‐related peaks exhibited particularly high relational degrees (> 0.8), corresponding to glucose, sucrose, kestose (GF₂), nystose (GF₃), and fructooligosaccharides GF₄–GF₇. Among these compounds, kestose and nystose showed particularly strong associations with the measured anti‐inflammatory responses. These findings identified several saccharides as candidate activity‐associated constituents for further evaluation.

3.3.2. PLSR Analysis and Model Validation

PLSR analysis further revealed distinct associations between chromatographic peak variables and the measured inflammatory responses. Peaks 1–6 exhibited regression coefficients in the negative direction, whereas peaks 7–14 exhibited coefficients in the positive direction (Figure 3). The regression coefficients were used to evaluate the direction and relative magnitude of the associations between individual chromatographic peaks and the biological response variables.

FIGURE 3.

Regression coefficient plot.

Variable importance in projection (VIP) analysis identified four saccharides with VIP values greater than 1.0: glucose, sucrose, kestose (GF₂), and nystose (GF₃) (Figure 4). Integration of the GRA and PLSR results therefore identified these four compounds as the leading activity‐associated saccharides within the saccharide‐focused analytical framework of the present study.

FIGURE 4.

VIP scores.

The robustness and predictive performance of the PLSR model were further evaluated. The model showed satisfactory explanatory capacity for the predictor variables (R²X = 0.836) and response variables (R²Y = 0.757), with a cross‐validated Q² value of 0.652. In the 200‐permutation test, the intercepts of the R² and Q² regression lines were 0.138 and −0.404, respectively, supporting the absence of substantial model overfitting (Figure 5). The overall statistical significance of the model was further supported by CV‐ANOVA (p = 0.001).

FIGURE 5.

Replacement test.

3.4. Biological Validation Results

3.4.1. Effects of Candidate Saccharides on NO, TNF‐α, IL‐1β, and IL‐6 Production

E The four candidate saccharides identified by spectrum‐effect relationship analysis—glucose, sucrose, kestose, and nystose—were subsequently evaluated in LPS‐stimulated RAW 264.7 macrophages. As shown in Figure 6, all four compounds reduced the production of NO, TNF‐α, IL‐1β, and IL‐6 in a concentration‐dependent manner. Among them, kestose and nystose generally exhibited stronger inhibitory effects and were therefore selected for subsequent mechanistic evaluation.

FIGURE 6.

Effects of glucose, sucrose, kestose, and nystose on the production of NO, TNF‐α, IL‐1β, and IL‐6 in LPS‐stimulated RAW 264.7 macrophages. RAW 264.7 cells were first stimulated with LPS (2 μg/mL) for 10 h. Following the removal of the supernatant, cells were then treated with indicated concentrations of the four saccharides for 12 h. Levels of NO, TNF‐α, IL‐1β, and IL‐6 in the culture supernatant were quantified using ELISA kits according to the manufacturers' protocols. Note: ^### p < 0.001 versus the Control group; *p < 0.05, **p < 0.01, ***p < 0.001 versus the LPS group.

For statistical analysis, the LPS model group was compared with the untreated control group to verify the inflammatory response, whereas each saccharide‐treated group was compared with the LPS model group to assess its inhibitory effect.

3.4.2. Western Blotting Analysis

Western blot analysis was performed to investigate whether NF‐κB signaling was involved in the anti‐inflammatory effects of kestose and nystose (Figure 7). HPLC quantification indicated that the concentrations of kestose and nystose corresponding to 0.2 mg/mL MOR extract were approximately 50 μM under the experimental conditions. Accordingly, 50 μM was selected as the treatment concentration for the mechanistic experiments.

FIGURE 7.

Effects of kestose and nystose (50 μM) on the NF‐κB signaling pathway in LPS‐induced RAW 264.7 macrophages. Representative Western blot images show protein levels of COX‐2, NF‐κB, p65, and p‐p65. Relative density of COX‐2. Relative density of IκBα. Statistical analysis of the p‐p65/p65 ratio (detected at 2 h after LPS stimulation, rightmost panel). Each bar represents the mean ± SD (n = 3). Note: ***p < 0.001, **p < 0.01, *p < 0.05; ^### p < 0.001, ^## p < 0.01, ^# p < 0.05; *versus CON group, ^#versus LPS group. CON: untreated control group. The blot shown is a representative image. Quantification was performed on three independent biological replicates, and statistical analysis is based on these quantitative measurements.

LPS stimulation significantly increased COX‐2 expression and the p‐p65/p65 ratio while decreasing IκBα protein levels (p < 0.01). Treatment with kestose or nystose significantly attenuated these LPS‐induced changes, as evidenced by reduced COX‐2 expression, a decreased p‐p65/p65 ratio, and restoration of IκBα levels (p < 0.01).

These findings support the involvement of NF‐κB pathway suppression in the anti‐inflammatory effects of kestose and nystose. Specifically, both oligosaccharides attenuated LPS‐induced IκBα loss and p65 phosphorylation and reduced downstream COX‐2 expression. Because the present analysis was performed using whole‐cell lysates without nuclear and cytoplasmic fractionation, NF‐κB nuclear translocation was not directly assessed.

4. Discussion

This study presents a comprehensive approach for evaluating the quality and anti‐inflammatory properties of MOR and its processed product, LB‐MOR, using HPLC‐ELSD fingerprinting combined with spectrum‐effect relationship analysis. Our findings demonstrate that processing with Lycium barbarum was associated with enhanced anti‐inflammatory activity and marked changes in the saccharide profile of MOR, particularly in several fructooligosaccharides. Through integrated GRA and PLSR analyses, four saccharides—glucose, sucrose, kestose, and nystose—were identified as important activity‐associated constituents within the saccharide fraction, with kestose and nystose exhibiting particularly strong anti‐inflammatory activity. These findings support an important contribution of saccharides to the observed anti‐inflammatory effects of MOR and LB‐MOR. However, they should not be interpreted as indicating that saccharides constitute the complete anti‐inflammatory material basis of M. officinalis , because this medicinal plant contains multiple classes of bioactive constituents that may also contribute to its overall pharmacological activity.

Mechanistic studies revealed that kestose and nystose exert anti‐inflammatory effects primarily through modulation of the NF‐κB signaling pathway. Western blot analysis confirmed that these compounds significantly inhibited LPS‐induced IκBα degradation and p65 phosphorylation, while also downregulating COX‐2 expression. These findings align with previous reports on oligosaccharide‐mediated anti‐inflammatory mechanisms and offer novel insights specific to MOR (Costa et al. 2022; Wongkrasant et al. 2020). The dose‐dependent suppression of pro‐inflammatory mediators, including NO, TNF‐α, IL‐1β, and IL‐6, further supports the anti‐inflammatory potential of these saccharides.

Importantly, the anti‐inflammatory activity of M. officinalis is not restricted to its saccharide constituents. Previous phytochemical and pharmacological studies have demonstrated that several non‐saccharide classes, including iridoids, iridoid glycosides, anthraquinones, naphthoates, and monoterpenes, also possess substantial anti‐inflammatory activity. Iridoid glycosides isolated or enriched from M. officinalis have been reported to suppress inflammatory mediator production and attenuate inflammatory responses through inhibition of NF‐κB and MAPK signaling pathways (Zhu et al. 2022; Cai et al. 2021). In addition, naphthoates and anthraquinones isolated from the roots of M. officinalis significantly inhibited LPS‐induced NO production in RAW 264.7 macrophages, reduced the expression of COX‐2 and iNOS, and blocked NF‐κB nuclear translocation (Luo et al. 2021). More recently, Jiang et al. (2024) identified a series of monoterpenes from M. officinalis , particularly iridoid constituents, with pronounced inhibitory effects on LPS‐induced NO production; selected compounds also downregulated COX‐2, iNOS, IL‐1β, and IL‐6 and interfered with NF‐κB nuclear translocation. Furthermore, M. officinalis iridoid glycosides have been reported to alleviate experimental rheumatoid arthritis through suppression of NF‐κB‐ and JAK2/STAT3‐related signaling (Shen et al. 2024). Collectively, these observations indicate that the overall anti‐inflammatory efficacy of M. officinalis is likely attributable to the combined contributions of multiple chemical classes rather than to saccharides alone. The saccharides identified in the present study should therefore be considered important anti‐inflammatory constituents within the specific analytical scope of our HPLC‐ELSD‐based investigation.

The canonical activation of the NF‐κB pathway is characterized by rapid early events, such as IκBα degradation and p65 phosphorylation, occurring within minutes to a few hours post‐stimulation, as established in the literature (Hobbs et al. 2020). Our experimental paradigm, involving compound administration 10 h post‐LPS challenge, was designed to investigate a distinct aspect: the modulatory capacity of the active saccharides on sustained NF‐κB signaling within an established inflammatory milieu. The assessment at this later time point reflects the steady‐state activity of the pathway, which is influenced by complex feedback loops rather than representing its initial trigger (O'Dea and Hoffmann 2010). The observed inhibition demonstrates a potent ability to suppress ongoing NF‐κB activation, which is functionally linked to the significant attenuation of downstream pro‐inflammatory cytokine production. This approach aligns with our objective of evaluating therapeutic potential against persistent inflammatory states. Future studies incorporating detailed kinetic analyses of early signaling events (0.5–4 h) will be invaluable to delineate the complete temporal inhibitory profile of kestose and nystose (Liu et al. 2017).

Quality assessment revealed notable batch‐to‐batch variation, particularly in samples S14 and P14, which exhibited similarity values below 0.980. This variability likely results from differences in harvest timing or suboptimal storage conditions affecting component stability. These findings underscore the importance of standardized production and storage protocols to ensure consistent pharmacological quality. The identified marker compounds—kestose and nystose—offer objective quality control parameters that could complement traditional evaluation methods.

The present study deliberately focused on saccharides because they are abundant constituents of MOR, undergo marked compositional changes during L. barbarum processing, and are particularly suitable for HPLC‐ELSD analysis because many sugars and fructooligosaccharides lack strong ultraviolet absorption. Consequently, the spectrum‐effect relationships established in this study represent activity‐associated constituents within a saccharide‐focused analytical window and cannot comprehensively account for the complete anti‐inflammatory material basis of MOR or LB‐MOR. In addition to the non‐saccharide constituents naturally present in MOR, constituents introduced from L. barbarum or generated through chemical transformation during processing may also contribute to the enhanced anti‐inflammatory activity of LB‐MOR. Therefore, the stronger activity observed after processing should not be attributed exclusively to changes in oligosaccharides. Future studies should integrate HPLC‐ELSD with complementary analytical platforms such as UPLC‐QTOF‐MS/MS or LC–MS/MS to achieve broader characterization of saccharides, iridoids, anthraquinones, and other secondary metabolites. Combining comprehensive chemical profiling with multivariate spectrum‐effect analysis, bioactivity‐guided fractionation, and in vivo validation will be important for determining the relative contributions and potential synergistic interactions of different constituent classes and for more comprehensively elucidating the anti‐inflammatory material basis of MOR and LB‐MOR.

Although this study provides evidence supporting the anti‐inflammatory effects of Morindae Officinalis Radix and its active saccharides through modulation of the NF‐κB pathway, certain methodological aspects should be noted. The assessment of NF‐κB activation in this work was based on the expression level of total cellular p65 protein measured in whole‐cell lysates. Although the observed changes are consistent with an inhibitory effect on this pathway, this approach has inherent limitations. Future studies will further elucidate these mechanisms by employing subcellular fractionation to directly evaluate NF‐κB nuclear translocation and by validating the therapeutic efficacy in relevant animal models of inflammation.

In conclusion, this study established an integrated HPLC‐ELSD fingerprinting, spectrum‐effect relationship, and biological validation strategy for investigating activity‐associated saccharide constituents in MOR and LB‐MOR. Comparative fingerprint analysis revealed processing‐related changes in the saccharide profiles of MOR, whereas GRA and PLSR identified glucose, sucrose, kestose, and nystose as the principal activity‐associated saccharides within the analytical scope of this study. Among these compounds, kestose and nystose exhibited relatively strong anti‐inflammatory effects and attenuated LPS‐induced inflammatory responses, at least in part, through suppression of NF‐κB signaling. Importantly, these findings should be interpreted within the context of the saccharide‐focused analytical approach used here, as non‐saccharide constituents may also contribute to the overall anti‐inflammatory activity of MOR and LB‐MOR. Collectively, this study provides an analytical and pharmacological basis for further characterization of processing‐induced chemical changes and supports the potential use of activity‐associated saccharides as candidate quality markers for the quality evaluation of MOR and its processed products.

Author Contributions

Kexu Dong: conceptualization, formal analysis and writing – original draft. Yuan Zhang: software. Zihang Peng: project administration. Jing Lian: methodology. Qiushi Hu: investigation. Yuexin Zhu: resources. Pengpeng Liu: validation. Guoshun Shan: project administration. Yixiang Miao: visualization. Yurou Feng: methodology. Fan Zhang: supervision. Ji Shi: supervision.

Funding

This work was supported by 2018 National Natural Science Foundation of China (81874345).

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.