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. 2024 Jan 17;33(7):1671–1683. doi: 10.1007/s10068-023-01476-x

Optimized combination of Cervus nippon (Sika deer), Angelica (Dangui), and Rehmannia (Suk-jihwang) mitigates LPS-induced inflammation: exploring signaling pathways through plasma metabolomics

Jeong Hoon Pan 1,2, Min Kook Lee 3,4,#, Moon Han Chang 3,4, Liana N Crowley 1, Brandy L Le 1, Da Seul Lee 5, Tae Gyun Kim 6, Dahye Kim 7, Kangwook Lee 3,8, Seong-Gyu Ko 8, Jin Hyup Lee 3,4,9,✉, Suk Hee Lee 3,4,9,✉, Jae Kyeom Kim 1,3,10,✉
PMCID: PMC11016027  PMID: 38623429

Abstract

This study aimed to determine the optimal combination of three anti-inflammatory materials [i.e., Cervus nippon Temminck (CT), Angelica gigas Nakai (AN), and Rehmannia glutinosa (RG)] for the strongest anti-inflammatory potential. Eighteen combinations of the three materials were tested in LPS-stimulated RAW264.7 cells via assessing nitric oxide (NO). The best combination from in vitro studies was administered to LPS-treated C57BL/6J mice for five days. Subsequently, plasma metabolites were profiled by bioinformatics analyses and validations. As results, 2, 20, and 50 µg/mL of CT, AN, and RG (TM) were the most effective combination suppressing inflammation. In mice, TM mitigated hepatic inflammatory markers. Similarly, the metabolomics indicated that TM may suppress NF-κB signaling pathway, thereby alleviating hepatic inflammation. TM also decreased systemic and hepatic pro-inflammatory cytokines. Collectively, we found the optimal combination of TM for mitigating inflammation; thus further studies on safety, mechanisms, and clinical models are warranted for human applications.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10068-023-01476-x.

Keywords: Cervus nippon Temminck, Angelica gigas Nakai, Rehmannia glutinosa, Unbiased approach, Mice, RAW264.7

Introduction

Alternative medicine includes medicinal preparations containing minerals, vitamins, nutritional supplements, herbs, and/or homeopathic remedies (Kisling and Stiegmann, 2022). The premise is that traditional medicines may replace pharmaceutical drugs which are often accompanied by side effects. Further, many efforts have been made to incorporate such ingredients, which are generally regarded as safe, into foods to promote functionality and add value. Evidence demonstrates that effective combinations of traditional medicines manipulate multiple targets, leading to amplification of their therapeutic effects (Cai et al., 2016; Deciga-Campos et al., 2021). Therefore, rather than a single agent/compound, combination therapy has become a rational and promising choice in multicomponent drug design (Lu et al., 2021). Cervus nippon Temminck (CT; sika deer), Angelica gigas Nakai (AN; dangui), and Rehmannia glutinosa (RG; suk-jihwang) have been used as materials for traditional medicines in Eastern Asia against inflammatory diseases such as chronic liver inflammation and fibrosis; the anti-inflammatory potential of each material has been demonstrated elsewhere (Baek et al., 2012; Cho et al., 2015; Ma et al., 2009; Wang et al., 2020). These three materials are commonly mixed together, yet there is no scientific evidence regarding their safety and optimal combination(s) against diseases such as inflammation.

Inflammation is a series of host protective responses of the immune system to infection and irritation (Chen et al., 2018). Macrophages, one of the most prevalent cytokine producers in the immune system, detect stress signals or pathogen-associated molecular patterns such as lipopolysaccharides (LPS) during the early onset of infection. Macrophages release a variety of pro-inflammatory mediators that include tumor-necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and nitric oxide (NO) (Kany et al., 2019). However, excessive production of pro-inflammatory mediators results in a chronic inflammatory state, which severely damages tissue and leads to various inflammatory diseases (Kany et al., 2019). Related, steroidal anti-inflammatory drugs (SAIDs) and nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly utilized as they are effective in controlling pro-inflammatory mediators (Pizarro and Cominelli, 2007). However, chronic use of SAIDs and NSAIDs can cause adverse effects, including gastrointestinal disorders, immunodeficiency, and humoral disturbances (Roth, 2012; Simon, 2003). Therefore, the optimized combination of traditional medicine (i.e., CT, AN, and RG) with fewer adverse effects will be an excellent alternative approach against diseases for long-term administration.

The aim of the present study was to find optimal concentrations of CT, AN, and RG to examine their combinatorial therapeutic effect against inflammation. For the optimization, effective concentrations of each material against LPS-induced inflammation without presenting serious cytotoxicity were screened, and then 18 mixture combinations were evaluated for anti-inflammatory activity through measuring NO production in vitro. Subsequently, the optimized combination of CT, AN, and RG was applied to an LPS-induced inflammation mice model. In the mice model, untargeted plasma metabolomics was performed to profile primary metabolites in response to the given treatment followed by bioinformatics and validation experiments.

Materials and methods

Preparation of CT, AN, and RG extracts

The extracts of the CT, root of AN, and root of RG samples were kindly provided by Kwang Dong Pharmaceutical Co., Ltd. (Seoul, Republic of Korea). Ground CT, AN, and RG (100 g) were extracted in 0.5 L of boiling water for 8 h [1:5 (w:v)], respectively. The extract was filtrated through filter paper, vacuum evaporated using a rotary evaporator, and then spray dried. The dry residue was stored at − 80 °C until used for in vitro experiments.

Cell culture

The murine RAW264.7 macrophage cell line was purchased from the American Type Culture Collection and cultured in high-glucose Dulbecco’s Modified Eagle Medium containing 10% fetal bovine serum (Thermo-Fisher Scientific, Waltham, MA, USA) and 1% penicillin (100 U/mL)/streptomycin (100 mg/mL) (Thermo-Fisher Scientific) at 37 °C in a humidified incubator containing 5% CO2.

Cell viability assay

To establish an in vitro model, lipopolysaccharide from Escherichia coli O111:B4 (LPS, Sigma-Aldrich, St. Louis, MO, USA; L4391) was used. In brief, after 24 h culture of RAW264.7 cells in a 96-well plate (2 × 104 cells/well, 100 μL medium/well), the cells were pre-treated with CT, AN, and RG for 2 h and then treated with 50 ng/mL LPS for an additional 24 h. Thereafter, thiazolyl blue tetrazolium bromide (MTT) (Sigma-Aldrich) solution was added to each well with a final concentration of 0.4 mg/mL and incubated at 37 °C for 1 h. Finally, the absorbance of each well was recorded at 490 nm using a microplate reader (Thermo-Fisher Scientific).

NO production assay

NO production was measured via colorimetric reactions using Griess reagents (Company, City, Headquarter). The RAW264.7 cells, at a density of 1 × 105 cells/well with 500 μL medium/well, were plated in a 24-well plate and then incubated for 24 h. Cells were then treated with CT, AN, and RG at various concentrations for 2 h followed by 50 ng/mL LPS for an additional 24 h. The accumulated NO in cultured supernatant (100 μL) from each well was evaluated with an equal volume of freshly mixed Griess reagent, containing 0.2% N-(1-naphthyl)-ethylenediamine dihydrochloride and 1% sulfanilamide in 5% phosphoric acid. The reaction mixtures in a 96-well plate were incubated at room temperature for 10 min and then absorbance was measured at 540 nm using a microplate reader (Thermo-Fisher Scientific). A standard curve of sodium nitrite was established to calculate the NO production level in each sample by the NO2− concentration.

Optimization of CT, AN, and RG concentrations

To investigate the optimal mixture ratio of CN, AN, and RG, the cells were pre-treated with various concentrations of CT, AN, and RG for 2 h followed by stimulation with LPS (50 ng/mL) for 24 h. Effects of individual materials on NO production were first investigated to find the best combination of CT, AN, and RG mixture. Two to three doses of each material were first examined in terms of suppressing NO levels in RAW264.7 cells. A total of 18 combinations were then compared (Supplementary Table 1).

Animal study design and sample treatment

All animal handling and experiments were performed in accordance with protocols approved by the Institutional Animal Care and Use Committee of the Korea University (Protocol Approval Number: 2019-0037). A total of 24 male C57BL/6J mice were initially assigned to 4 groups: negative control [NEG group (n = 6)], LPS control [LPS group (n = 6); 15 mg/kg; intraperitoneally (i.p.) injected], and LPS and traditional medicine mixture [TM groups; 50 mg/kg body weight/day (n = 6) and 100 mg/kg body weight/day (n = 6); oral gavage]. When it comes to doses, since CT elicited the most significant cytotoxicity, relevant literature in which CT extract was administered were reviewed. Based on the literature (Choi et al., 2020; Wu et al., 2013), we decided to test 50 mg/kg and 100 mg/kg TM to see if such effects are dose-dependent. The American Institute of Nutrition-93G diet was used as a basal diet for all groups. LPS was i.p. injected once, 12 h prior to the start of TM intervention. For TM intervention, 50 mg/kg (i.e., 0.7 mg, 14 mg, and 35 mg of CT, AN, and RG extracts dissolved in normal saline) and 100 mg/kg (1.4 mg, 28 mg, and 70 mg of CT, AN, and RG extracts dissolved in normal saline) were orally administrated once a day over 5 consecutive days. NEG group mice were treated with normal saline via i.p. injection and oral gavage in parallel with LPS and sample treatments. After the TM intervention, all mice were euthanized by exsanguination to harvest tissues and blood. The study design and overall procedure is described in Supplementary Fig. 1A. The collected tissues were weighed and either stored in RNALater (Thermo-Fisher Scientific) at -80ºC or fixed with 10% neutral buffered formalin solution until further analyzed.

Histological analysis and immunofluorescence

Paraffin-embedded liver tissue sections (3 mm thickness) were stained with hematoxylin & eosin Y (H&E). The severity of inflammation was assessed by histomorphological observation. Tissue integrity, neutrophil infiltration, and fat accumulation were assessed as indicators of liver injury from H&E-stained sections. MMP-2 expression was examined via immunofluorescence staining in the liver tissue sections. Stained sections were observed using a Zeiss Axiovert 200 inverted microscope (Oberkochen, Germany).

Messenger RNA expression analysis

RAW264.7 cells in 24 well plates at a density of 1 × 105/well were pretreated with TM for 2 h prior to adding 50 ng/mL LPS for 24 h. The total RNA of RAW264.7 cells was extracted using the TRIzol (Thermo-Fisher Scientific) method and 1 µg of total RNA was reverse transcribed to cDNA using the RT pre-Mix cDNA synthesis Kit (Biofact, Daejeon, Republic of Korea) according to the manufacturer’s protocol. Liver tissue RNA was also isolated using the TRIzol method. One microgram of total RNA was reverse transcribed using the RT pre-Mix cDNA synthesis Kit. mRNA expression was measured by quantitative reverse transcription PCR (qPCR) analysis using the ABI 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) in a reaction mixture containing GoTaq qPCR Master Mix (Promega; Madison, WI, USA), primers, and cDNA. PCR amplification was conducted under the following conditions: one cycle at 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles of denaturation (95 °C for 15 s) and annealing (60 °C for 1 min). Genes of interest were normalized with a reference gene, GAPDH. Quantification cycle (Ct) values were recorded, and the relative expression levels of target genes were calculated using the 2−ΔΔCT method with 7500 Software (Ver. 2.1; Applied Biosystems). All samples were run in triplicate, and primer information is available in Supplementary Table 2.

Plasma metabolomics

Frozen mice plasma samples were shipped to the West Coast Metabolomics Center (WCMC; University of California, Davis Genomic Center, Davis, CA, USA) for primary metabolomics analysis. The plasma samples were then extracted, derivatized, and subjected to gas chromatography time-of-flight mass spectrometry (GC-TOF MS), as described elsewhere (Fiehn et al., 2010; Fiehn and Kind, 2007). Data were processed using the BinBase database at the WCMC; data were filtered to remove noise peaks. Normalization of ion peak heights was performed by the sum intensity of all annotated metabolites, which was utilized for further statistical analyses. The experimental flow is summarized in Supplementary Fig. 1B.

Bioinformatic analyses

Plasma primary metabolomics data were log10 transformed and then normalized to the sample median using the Statistical Analysis tool available at MetaboAnalyst 5.0 online. The autoscaled dataset was subjected to principal component analysis (PCA) to find the directions that best account for the variance in the dataset. PC scores were plotted based on PC loadings that are the weighted average of the original variables. Additionally, the metabolites that contributed most to PC loadings for PC1 and PC2 were utilized to annotate key diseases or biological functions in the metabolomics dataset using Ingenuity Pathway Analysis (IPA) software. The overlay function in the IPA Core Analysis was utilized to compare results from Networks Analysis and Diseases and Functions Analysis features. Key candidate markers relevant to the link between metabolites in a predicted network and disease were selected for further analyses. Moreover, Upstream Regulator analysis in the IPA Core Analysis was performed to identify potential key molecules (e.g., transcription factors, genes, drugs) that may account for the observed plasma metabolomic signature.

Cytokine array

The level of cytokines in the mouse plasma were quantified using the Proteome profiler mouse cytokine array kit (R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s instructions. Briefly, the plasma was diluted and mixed with antibody cocktail. The mixture was then applied to the array membrane and incubated overnight at 4 °C. The membrane was washed and incubated with streptavidin–horseradish peroxidase (HRP) buffer for 30 min. The array images of dot blots were quantified using ImageJ software [National Institute of Health (NIH), Bethesda, MD, USA] and described as a heatmap table.

Immunoblot analysis

Protein expression was assessed using immunoblotting. In brief, protein samples (1 mg/mL) were dissolved in 1× sample buffer and then heated for 10 min to denature proteins. After, the samples were separated via SDS-PAGE and then transferred to a nitrocellulose membrane. Membranes were blocked in blocking buffer containing 5% BSA in Tris-buffered saline (0.5 M Tris base, 9% NaCl, and 1% Tween 20; pH 7.8) for 1 h followed by incubation with primary antibodies for 12 h at 4 °C. The membranes were then incubated with the HRP conjugated secondary antibodies for 1 h at room temperature. The protein bands were detected using the ImageQuant LAS 4000 (GE Healthcare Life Sciences; Marlborough, MA, USA) and their intensity was quantified using ImageJ software (NIH). Each membrane included a reference sample, which was used in all blots, and the results were calculated as the ratio of protein/β-actin divided by the ratio of the reference sample/β-actin to factor in inter-assay variation. Antibody information is provided in Supplementary Table 3.

Statistical analysis

All experiments were at least triplicated and results were expressed as mean ± standard error of mean (SEM). Differences between the groups were tested using one-way ANOVA followed by Tukey’s post-hoc test for multiple comparisons using GraphPad Prism Software Ver 9.3.1 (GraphPad; San Diego, CA, USA). A p value of 0.05 or less was considered statistically significant.

Results and discussion

Determination of optimal concentrations of CT, AN, or RG against LPS-induced NO level in RAW264.7 macrophage cell line

Maximum concentrations of each material without presenting cytotoxicity were determined using RAW264.7 macrophage cell lines. The correct non-toxic concentration of RG is 500 μg/mL, not 200 μg/mL. According to the results of MTT assay, CT, AN, and RG presented cytotoxicity at 20, 100, and 500 µg/mL, respectively (Fig. 1A–C). Based on the results, four non-toxic concentrations were tested for each material in terms of NO production. Released NO from active macrophages acts as a pro-inflammatory mediator that induces inflammatory signaling under abnormal conditions (Tripathi et al., 2007). In the NO production assay, 50 ng/mL LPS was treated to activate RAW264.7 macrophages without killing cells (Supplementary Fig. 2A–D). All three extracts showed a dose-dependent suppression of NO production against LPS-induced RAW264.7 macrophage activation (Fig. 1D–F). However, 10 µg/mL CT, 50 µg/mL AN, and 100 µg/mL RG concentrations activated RAW264.7 macrophages without LPS stimulation despite their non-cytotoxic property. Thus, we excluded 10 µg/mL CT, 50 µg/mL AN, and 100 µg/mL RG in further optimization processes.

Fig. 1.

Fig. 1

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Effect of CT, AN, and RG on cell viability and NO production. (A–C) RAW264.7 cells were treated with indicated doses of each extract for 24 h, and cell viability was determined by MTT assay. (D-F) Four non-cytotoxic doses of each extract were pre-treated for 2 h in unstimulated (white bar) and stimulated with 50 ng/mL LPS (grey bar) RAW264.7 cells and NO level in cell supernatants was measured by Griess reagent. A p value less than 0.05 was considered statistically significant (*p < 0.05 or #p < 0.05 vs respective 0; n = 3/group). AN Angelica gigas Nakai, CT Cervus nippon Temminck, RG Rehmannia glutinosa, NO nitric oxide, MTT thiazolyl blue tetrazolium bromide, LPS lipopolysaccharide

Establishment of optimal combination of CT, AN, and RG mixture (TM) against LPS-induced inflammation in RAW264.7 macrophage cell line

A total of 18 combinations of CT, AN, and RG extracts were prepared based on the individual effect on NO production; 1, 2, and 5 µg/mL CT extracts, 10 and 20 µg/mL AN extracts, and 10, 20, and 50 µg/mL RG extracts were used to make the 18 combinations. Importantly, none of the 18 combinations were toxic to cells (Supplementary Fig. 2E). As for CT, there was no additional potency between 2 and 5 µg/mL concentrations when co-treated with AN and RG extracts (Fig. 2A), which led us to select 2 µg/mL CT concentration for further experiments. Second, regardless of formulation combinations, AN and RG showed strong NO suppression effects at 20 and 50 µg/mL, respectively. Importantly, while RG (50 µg/mL), AN (20 µg/mL), and CT (2 µg/mL) individually exhibited inhibition rates of 47.13%, 57.56%, and 23.88%, respectively, in comparison to the LPS group within the NO assay, TM showed a notably higher inhibition rate of 64.73% when compared to the LPS group. Collectively, 2 µg/mL CT, 20 µg/mL AN, and 50 µg/mL RG were considered the best combination for NO suppression without cytotoxicity (Fig. 2B) in RAW264.7 macrophages. In our follow up tests, this combination of CT, AN, and RG mixture (TM) significantly decreased mRNA levels of TNFα, IL1β, and IL-6 pro-inflammatory cytokines in LPS-stimulated RAW264.7 macrophages (Fig. 2C).

Fig. 2.

Fig. 2

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Optimization of CT, AN, and RG mixture. (A) A total of 18 different combinations of CT (1, 2, and 5 µg/mL), AN (10 and 20 µg/mL), and RG (10, 20, and 50 µg /mL) were compared in LPS-stimulated RAW264.7 cells and NO production was measured. (B) Effect of the selected combination (indicated by TM; 2 µg/mL of CT, 20 µg/mL of AN, and 50 µg/mL of RG) on RAW264.7 cell viability was measured by MTT assay. (C) Effect of TM on expression of TNFα, IL1β, and IL-6 in LPS-stimulated RAW264.7 cells. A p value less than 0.05 was considered statistically significant (*p < 0.05; n = 3/group). AN Angelica gigas Nakai, CT Cervus nippon Temminck, RG Rehmannia glutinosa, NO nitric oxide, LPS lipopolysaccharide, TM optimized mixture of CT, AN, and RG

In our study, we aimed to create combinations of three TMs based on their individual effects on cytotoxicity and NO production in RAW264.7 cells. While we acknowledge that the method may appear simplistic, authors believe it is appropriate for the scope and objectives of the present study, namely, to identify the best combination against inflammation. To be more specific, first, we used the MTT cell viability assay to determine the maximum non-cytotoxic concentrations of each TM. This assay is a widely accepted and commonly used method for evaluating cell viability. By selecting concentrations that did not exhibit cytotoxicity, we ensured the viability of the cells during subsequent experiments. Moreover, since NO plays a role in every stage of the immune response associated with inflammation, serving as a crucial mediator that promotes inflammation through excessive production during abnormal circumstances, we evaluated the inhibitory effects of each TM on NO production in RAW264.7 cells. This step allowed us to identify the individual potential of each TM in suppressing NO production, which is an important parameter in our study. To determine the optimal mixture of TMs, we systematically combined different concentrations of the individual TMs based on their individual effects on NO production. We created a total of 18 combinations and assessed their cytotoxicity and NO suppression properties. Importantly, we ensured that none of the 18 combinations exhibited cytotoxicity to the cells. This step was crucial to guarantee the safety of the combinations for further investigation. Finally, we selected the best combination, consisting of 2 µg/mL CT, 20 µg/mL AN, and 50 µg/mL RG, based on its ability to suppress NO production without causing cytotoxicity. This selection was made by carefully considering the individual effects of each TM and aiming for the most effective and safe combination. We believe that our approach is appropriate for the initial stages of research, where the primary focus is on assessing the individual effects of each material and identifying combinations that exhibit desirable properties. Further studies can certainly explore more comprehensive methods for optimization and fine-tuning of the mixture ratios.

Anti-inflammatory effect of TM against LPS-induced hepatic inflammation in C57BL/6J mice

The TM was orally administrated to mice 12 h after LPS treatment to examine efficacy against hepatic inflammation. Hematoxylin & eosin staining was carried out to observe the pathological changes of liver tissue. LPS-induced inflammatory cell infiltration and hepatic fat accumulation were significantly lowered in TM-fed mice (Fig. 3A; Supplementary Fig. 3); LPS transiently induces fat accumulation in the liver (Ohhira et al., 2007), which supports our result. The decreased hepatic fat accumulation in the TM group is likely due to the combined effects of TM; multiple articles have reported the benefits of CT, AN, or RG individually against fat accumulation (Bae et al., 2017; Ding et al., 2017; Park et al., 2017).

Fig. 3.

Fig. 3

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Effect of TM on LPS-induced hepatic inflammation. (A) H&E-stained liver sections of LPS + TM group were compared to CON and LPS groups. Infiltrated immune cells (arrow) and lipid droplet (arrowhead) in LPS-injected mouse liver. (B) Immunofluorescence microscopy of MMP-2 (green) expression in mouse liver. Images were acquired and processed under identical conditions. Scale bar 50 mm. CON normal control, LPS lipopolysaccharide, TM optimized mixture of Angelica gigas Nakai, Cervus nippon Temminck, and Rehmannia glutinosa

Related, MMP-2, also called gelatinase A, plays a critical role in the progression of hepatic inflammation and fibrosis. Studies reported increased MMP-2 expression in liver injury including hepatic fibrosis and hepatitis (Duarte et al., 2015). Since NO, NF-κB, and inflammatory cytokines (e.g., IL-1β and IL-6) are the key regulators of MMP-2 expression and activity (Eberhardt et al., 2000; Kusano et al., 1998; Lan et al., 2009), we examined the expression of MMP-2 through immunofluorescence staining. As expected, increased MMP-2 expression was observed in LPS-treated mice liver tissues compared to normal control mice and was reversed in TM-treated mice liver tissues (Fig. 3B; Supplementary Fig. 3).

Effect of TM on primary metabolites in plasma: bioinformatics analyses

We aimed to explore how plasma metabolomic signatures are associated with disease phenotypes in the LPS-treated mice livers. First, there were no changes in body weight and liver tissue weight (Supplementary Table 4). Bioinformatic analyses were performed using the plasma primary metabolomics dataset. PCA showed well-segregated patterns of plasma metabolites (Fig. 4A). More specifically, PCA plot for PC1 and PC2 explained 57% of the total variance (29.6% for PC1 and 27.4% for PC2; Fig. 4B), which indicates that PC1 and PC2 almost equally comprise the variance information between the groups. The top 30 metabolites that contributed to the loadings of PC1 and PC2 representing the separation between LPS and LPS + TM are shown in Supplementary Table 5. The top 30 metabolites for each PC (i.e., 60 metabolites total) were utilized for further Network analysis and Biological Function analysis using the IPA software, followed by the IPA Core analysis.

Fig. 4.

Fig. 4

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Effect of TM on primary metabolic profiles in mice plasma. (A) PCA plot of the identified metabolites. (B) Scree plot shows the variance explained by five components. The green and blue lines represent the accumulated variance and variance of individual PC explained, respectively. (C) Most enriched network and biological functions in featured metabolites contributed to PC1 and PC2 representing the separation between LPS and LPS + TM. The network was produced by the IPA software. (D) Key featured metabolites patterns that are associated with ‘Inflammation of organ’ function in the IPA network. CON normal control, IPA ingenuity pathway analysis, LPS lipopolysaccharide, TM optimized mixture of Angelica gigas Nakai, Cervus nippon Temminck, and Rehmannia glutinosa, PC principal component, PCA principal component analysis

The Network analysis of the IPA Core analysis listed 12 networks that are involved in the plasma metabolomics dataset. The Network #1 (rank) showed the strongest association with the metabolomics dataset with a network score of 57 and 20 focus molecules (Table 1). The Network #2 presented only a network score of 7 with 4 focus molecules, indicating that Network #1 is the dominantly enriched network in the PC1 and PC2 contributing metabolites. Subsequently, the Network #1 was overlayed with a ‘Disease & Function’ list to link relevant diseases or functions, of which, ‘Organismal Injury and Abnormalities’ was the most enriched in our dataset with 34 related molecules.

Table 1.

18 Combinations of CT, AN, and RG extracts

Combinations Concentration (μg/mL)
CT AN RG
1 1 10 10
2 1 10 20
3 1 10 50
4 1 20 10
5 1 20 20
6 1 20 50
7 2 10 10
8 2 10 20
9 2 10 50
10 2 20 10
11 2 20 20
12 2 20 50
13 5 10 10
14 5 10 20
15 5 10 50
16 5 20 10
17 5 20 20
18 5 20 50
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AN Angelica gigas Nakai, CT Cervus nippon Temminck, RG Rehmannia glutinosa

Among multiple diseases and functions, 15 molecules (e.g., l-aspartic acid, l-cysteine, and l-palmitic acid) related to ‘inflammation of organ’ were changed by TM treatment (p = 0.0009; Fig. 4C). Notably, pro-inflammatory cytokines were predicted to be inhibited in TM-treated mice, which includes IL-1β, IL-6, and TNF (Fig. 4C). In the present study, suppression of pro-inflammatory cytokines was associated with decreased fatty acids (e.g., palmitic acid, stearic acid, and arachidonic acid) and with increased amino acids (e.g., cysteine, glutamine; Fig. 4D). Pro-inflammatory roles of palmitic acid and stearic acid are well-known; for example, palmitic acid acts as an agonist of toll-like receptors (Korbecki and Bajdak-Rusinek, 2019). Accumulated stearic acid in macrophages also induces inflammation via activating NF-κB pathway-related pro-inflammatory cytokines (Anderson et al., 2012). Similarly, arachidonic acid can produce both pro-inflammatory cytokines and anti-inflammatory eicosanoids via arachidonic acid metabolism (Wang et al., 2021). As for the amino acids, there is much evidence demonstrating critical roles of amino acids in liver disease and inflammatory diseases (Lee and Kim, 2019; Liu et al., 2017). However, it should be noted that an increase or decrease in fatty acids and amino acids cannot be simply justified; rather, it should be interpreted in the context of homeostasis of human physiology. However, as we see clear patterns between groups, it is worth further exploration of markers of inflammation such as inflammatory cytokines as well as validation and bioinformatics analyses.

Attenuation of LPS-induced systemic inflammation via NF-κB signaling pathway by TM

To validate the effect of TM against systemic inflammation, a cytokine array was conducted using plasma samples from LPS-treated mice (Supplementary Table 6). As expected, LPS clearly increased most cytokines including IL-6 and TNFα (Fig. 5A). As indicated in the heatmap, a trend of decrease in 16 cytokines was demonstrated by TM treatment (Fig. 5B). These cytokines were subjected to the IPA to predict a cytokine network with associated molecules. Most of the selected cytokines (15 out of 16 cytokines) were associated with the cytokine network and TNFα, CCL5, and CXCL10 were recognized as core molecules of the network (Fig. 5C). Of note, NF-κB-RelA (i.e., p65 transcript) was shown as an upstream transcription factor for TNFα and CCL5 (Fig. 5C; highlighted with yellow circle), indicating that TM might be modulating the NF-κB signaling pathway. Further, our cytokine network predicted that NF-κB is involved in the decreased expression of inflammatory cytokines by TM. Also, hepatic mRNA expression showed that LPS-induced upregulation of IL-6 (p = 0.05) and TNFα (p = 0.005), target genes of NF-κB pathway, was reversed in TM mice (Fig. 5D). Last, we examined key protein expression of NF-κB signaling pathways; protein expressions of NF-κB p65 subunit (p65), NF-κB inhibitor alpha (IκB), and IκB kinase (IKK) were examined. Despite the marginal decrease of IKK expression by TM with a p value of 0.07, TM significantly decreased both phosphorylated IκB (p-IκB) and p-p65 expressions compared to those of LPS group (Fig. 5E). We additionally measured mRNA expression of inducible NO synthase (iNOS) that triggers NO generation in RAW264.7 macrophage cells. LPS significantly increased iNOS expression and TM lowered it as low as control levels (Supplementary Fig. 2F). Since free radical scavenging effects of individual samples (CT, AN, and RG) were previously reported elsewhere (Heo et al., 2010; Je et al., 2010; Jeong et al., 2013; Noh et al., 2014), we examined one of the key antioxidant enzymes, superoxide dismutase 2 (SOD2) which is an indirect regulator of iNOS. Unexpectedly, SOD2 level was also elevated by LPS and decreased in LPS + TM treated cells (Supplementary Fig. 2F), which could be a defensive response to the LPS toxicity.

Fig. 5.

Fig. 5

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Effect of TM on systemic and hepatic inflammatory cytokines. (A) Dot blot analysis of 40 inflammatory cytokines, and (B) heatmap of 16 featured cytokines. Color key depicts intensity of cytokines. (C) Interaction cytokine network of the 16 featured cytokines using the IPA software. The IPA network analysis predicted up- and down-stream molecules modulating the cytokines. The prediction was experimentally validated via (D) quantitative PCR analysis (n = 6/group) and (E) further elaborated on related signaling pathway via western blot analysis (n = 3–6/group). A p value less than 0.05 was considered statistically significant (**p < 0.01; ***p < 0.001; ****p < 0.0001). CON normal control, IPA ingenuity pathway analysis, LPS lipopolysaccharide, TM optimized mixture of Angelica gigas Nakai, Cervus nippon Temminck, and Rehmannia glutinosa, PCR polymerase chain reaction

The present study demonstrated the inhibitory effect of TM on NF-κB pathway in LPS-induced inflammation, which pathway is generally recognized as the inflammatory master marker. In this paragraph, we introduced some anti-inflammatory compounds targeting NF-κB pathways in CT, AN, and RG. Anti-inflammatory compounds in CT, including chondroitin and inosine, has been reported (Li et al., 2013; Zong et al., 2014). Chondroitin sulfate mitigates experimental osteoarthritis by regulating activity of NF-κB pathway (Korotkyi et al., 2021; Stabler et al., 2017). Moreover, chondroitin sulfate prevents peritoneal fibrosis in mice model by inhibiting NF-κB activity (Abe et al., 2016). Pretreatment of inosine ameliorates LPS-induced lung damage through suppressing NF-κB signaling pathway in vivo (Mao et al., 2022). Moreover, microbiome-derived inosine shows protective effect on LPS-induced acute liver damage and inflammation in mice via regulating NF-κB pathway (Guo et al., 2021). Among several compounds in AN, ligustilide, nodakenin and decursinol angelate were found active compounds with anti-inflammatory properties (Ahn et al., 2008; Choi et al., 2018; Gui and Zheng, 2019; Jeong et al., 2015; Kim et al., 2016, 2018; Lee et al., 2017; Li et al., 2020; Piao et al., 2007). Choi et al. demonstrated that ligustilide, a bioactive phthalide derivative isolated from angelica gigas Nakai root, attenuates vascular inflammation via inhibiting TNF- α-activated NF-κB signaling pathway in human vascular endothelial cells (Choi et al., 2018). Moreover, nodakenin exhibits anti-inflammatory and anti-fibrotic properties with down-regulation of Smad3, NF-κB phosphorylation and Snail1 expression (Li et al., 2020). In addition, nodakenin inhibits mast cell-mediated allergic inflammation via downregulation of NF-κB activation (Lee et al., 2017). Inhibitory effect of decursinol angelate on NF-κB signaling pathway in inflammatory disease in vitro and in vivo models has been reported (Chang et al., 2019; Islam et al., 2018; Kim et al., 2010; Pak et al., 2021). Furthermore, RG also includes anti-inflammatory compounds such as acteoside, catalpol (Gong et al., 2019; Li et al., 2011). Acteoside mitigates LPS-induced inflammatory response in acute lung injury via suppressing NF-κB pathway in vitro and in vivo (Jing et al., 2015; Song et al., 2016). Catalpol also inhibits NF-κB activity in experimental models of chronic kidney disease, psoriasis and insulin-resistance (Liu et al., 2021; Zaaba et al., 2023; Zhou et al., 2015). To sum up, the phytochemicals found in CT, AN, and RG have the potential to target the NF-κB pathway, indicating that these compounds may synergistically exhibit a modulatory effect on multiple pathways to combat inflammation.

To summarize, LPS-induced liver injury is associated with an increase in inflammatory cytokines (e.g., IL-1β, IL-6, TNFα) via activation of the NF-κB signaling pathway (Liu et al., 2018). NF-κB activation is triggered by the IKK complex that leads to proteasomal degradation of IκBα by phosphorylation, resulting in the activation of the NF-κB cascade (Hacker and Karin, 2006). In the present study, metabolomic profiling predicted that TM-derived metabolites (i.e., fatty acids and amino acids) might be key molecules contributing to the suppression of LPS-induced inflammation via the activation of NF-κB.

Collectively, this study was conducted to determine the optimal combination of medicinal materials (i.e., CT, AN, and RG) traditionally used in East Asian countries. The optimal combination for CT, AN, and RG extracts (i.e., TM) was determined based on NO production in RAW264.7 macrophage cells followed by in vivo confirmation using LPS-treated mice livers. Pro-inflammatory cytokines were suppressed in both in vitro and in vivo models. Further, our plasma metabolomics indicated pro-inflammatory cytokines would be suppressed by TM treatment, leading us to further experimentally validate if TM inhibited activation of NF-κB as a key signaling pathway. The present study provides an optimal combination of TM for mitigating inflammation in vitro which was also effective in LPS-treated mice even via unbiased approaches (i.e., plasma metabolomics, and cytokine array).

There are weaknesses to be addressed. We utilized a small sample size for the animal study (n = 6 per group), which may explain the marginal effects of some markers. Despite the small sample size, the metabolomics dataset showed a clear separation between groups in PCA analysis, and we also performed multi-layered validations using a cytokine array panel, PCR, and immunoblot assays for the same pathway to compensate for the limitation. In addition, the identification of active compounds present in natural products is an important aspect of the study. However, we would like to emphasize that all three extracts have been widely utilized as a traditional medicine either alone or mixed together. Also, due to their long history and wide applications, previous studies have characterized active compounds from the materials. For instance, functional peptides, coumarin derivatives, and phenolic constituents were profiled using CT, AN, and RG, respectively (Ahn et al., 1996; Park et al., 2019; Sui et al., 2014; Zhang et al., 2012). Instead, we believe that finding optimal concentrations, as a whole, for maximal biological potential and metabolomics signature in response to the TM would be much more novel information. Lastly, we did not include a positive drug group since our study is not for a new drug development and is not specific to a certain disease. However, future studies may benefit from the inclusion of a positive drug group. Collectively, the study provided novel findings applicable to future studies on developing TM-based anti-inflammatory functional foods.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PPTX 19838 KB) (19.4MB, pptx)
Supplementary file2 (PPTX 18366 KB) (17.9MB, pptx)
Supplementary file3 (DOCX 79 KB) (82.6KB, docx)

Author contributions

Conceptualization: SHL, JKK, JHL; Methodology: JHP, JHL; Software: JHP, MKL; Validation: DSL, TGK; Formal analysis: JHP, MKL, MHC, DSL, TGK; Investigation: JHP, MKL, MHC, LNC, BLL; Resources: SHL, JHL, JKK; Data curation: JHP, SHL; Writing – Original Draft: JHP, MKL, LNC, BLL; Writing – Review & Editing: all authors; Visualization: JHP, MKL; Supervision: SHL, JKK, JHL; Project administration: SHL, JHP; Funding acquisition: JHL, JKK.

Funding

This work was supported by Kwang Dong Pharmaceutical Co., LTD (JHL and JKK), University of Delaware Start-Up fund, and Center of Biomedical Research Excellence in Cardiovascular Health (NIH NIGMS, P20GM113125-03) (JKK), the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C1011929) (JHL) and the Ministry of Education (RS-2023-00245564) (JKK), and by the BK21 FOUR program through the National Research Foundation (NRF) funded by the Ministry of Education of Korea (JHL).

Data availability

The datasets generated during the current study are not publicly available yet due to confidentiality regarding a patent preparation but are available from the corresponding authors on reasonable request.

Declarations

Competing interests

The authors have no conflict of interest to disclose.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Jeong Hoon Pan and Min Kook Lee have contributed equally.

Contributor Information

Jin Hyup Lee, Email: jinhyuplee@korea.ac.kr.

Suk Hee Lee, Email: sukheelee@korea.ac.kr.

Jae Kyeom Kim, Email: jkkim@udel.edu.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (PPTX 19838 KB) (19.4MB, pptx)
Supplementary file2 (PPTX 18366 KB) (17.9MB, pptx)
Supplementary file3 (DOCX 79 KB) (82.6KB, docx)

Data Availability Statement

The datasets generated during the current study are not publicly available yet due to confidentiality regarding a patent preparation but are available from the corresponding authors on reasonable request.

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